{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "635f1bab-a491-4dac-acf6-9e94d118fbda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Avg. Area Number of Bedrooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.682861</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>4.09</td>\n",
       "      <td>23086.80050</td>\n",
       "      <td>1.059034e+06</td>\n",
       "      <td>208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>6.002900</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>3.09</td>\n",
       "      <td>40173.07217</td>\n",
       "      <td>1.505891e+06</td>\n",
       "      <td>188 Johnson Views Suite 079\\nLake Kathleen, CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.865890</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>5.13</td>\n",
       "      <td>36882.15940</td>\n",
       "      <td>1.058988e+06</td>\n",
       "      <td>9127 Elizabeth Stravenue\\nDanieltown, WI 06482...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>7.188236</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>3.26</td>\n",
       "      <td>34310.24283</td>\n",
       "      <td>1.260617e+06</td>\n",
       "      <td>USS Barnett\\nFPO AP 44820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.040555</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>4.23</td>\n",
       "      <td>26354.10947</td>\n",
       "      <td>6.309435e+05</td>\n",
       "      <td>USNS Raymond\\nFPO AE 09386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0       79545.45857             5.682861                   7.009188   \n",
       "1       79248.64245             6.002900                   6.730821   \n",
       "2       61287.06718             5.865890                   8.512727   \n",
       "3       63345.24005             7.188236                   5.586729   \n",
       "4       59982.19723             5.040555                   7.839388   \n",
       "\n",
       "   Avg. Area Number of Bedrooms  Area Population         Price  \\\n",
       "0                          4.09      23086.80050  1.059034e+06   \n",
       "1                          3.09      40173.07217  1.505891e+06   \n",
       "2                          5.13      36882.15940  1.058988e+06   \n",
       "3                          3.26      34310.24283  1.260617e+06   \n",
       "4                          4.23      26354.10947  6.309435e+05   \n",
       "\n",
       "                                             Address  \n",
       "0  208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...  \n",
       "1  188 Johnson Views Suite 079\\nLake Kathleen, CA...  \n",
       "2  9127 Elizabeth Stravenue\\nDanieltown, WI 06482...  \n",
       "3                          USS Barnett\\nFPO AP 44820  \n",
       "4                         USNS Raymond\\nFPO AE 09386  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv('USA_Housing.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "85b567d8-9345-44c9-926d-e444de934fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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ooYcwceJElJWV4YUXXkBISAief/55AC1pxF9++WXcf//9GDt2LP7+97+jvr4er7zyCiorK7F48WLZ2jhu3DhzbarWrVvjzJkzWLZsGTp16oSuXbtKfj2a6BBCCCGEKMR7772HPn36YPXq1XjvvfcQGhqK5ORkzJ0713zNnDlzkJycjNdeew0bNmxAfX09DAYD+vXrh8cff1z095o8eTK2bt2KmJgY3H777XZfHzBgAB577DHs3r0bH374IUpLS83teeONNzhDz8TQaDRYsmQJbrvtNruvpaen4/3338fixYsxYcIEdO7cGc8//zx27twpKtGCVKmpqXj00Ufx/PPP49SpU2jXrh3+7//+z2qSodfr8e2332Lx4sV4++23UVRUhNDQUHTs2BHDhw93ekcHaNm969KlC1avXo0VK1YgMjISo0aNwqJFi8whY3J6/vnn8cEHH+Df//43cnJyMGfOHLRp0wavv/46Nm7caK5F9NJLL1lNLCZNmgS9Xo9Fixbh3nvvRUBAAAYMGIDdu3fjlltuka19Q4cOxaZNm/Duu++iuroaBoMBI0aMwPz586HT6SS/noZhbJJ/E0IIIYQQQojK0RkdQgghhBBCiM+hiQ4hhBBCCCHE59BEx0Pee+89aDQa83+BgYG47rrr8Oijj+LcuXOiXuORRx5xKQ5UqpkzZ0Kj0Zgzq3CZN28eNBqNuahWTU0NlixZgl69eiEiIgLh4eHo0qUL7rnnHuzdu1fwe2o0GkybNk22n4EQpaG+wPW+4LPPPuMs0qdmd955J/V/hBAiM5roeNjatWuRm5uLXbt2YcqUKdiwYQMGDRqEmpoawefOnz8fmzdv9kArW7AZWPjyxptMJnzwwQdITU1Fnz590NzcjNtuuw0vvvgi7r77bnz66af47LPPMHPmTFRVVeHbb7/1WNsJUTrqCwjr4sWL2L59OwDgo48+sitoSAghxEkM8Yi1a9cyAJgffvjB6vH58+czAJh169bxPrempsbdzePVv39/xmAwMI2NjXZf+/LLLxkAzBtvvMEwDMPk5OQwAJg1a9ZwvlZzc7Pg9wPATJ061bVGE6Jg1Be43hd8+umnDABm9+7d4n8ABXvllVcYAMyYMWMYAMxHH33k7SYRQohPoB0dLxswYAAA4MyZMwBaQlJatWqFn376CbfddhvCw8PNBZu4wlVMJhPeeOMNpKamIjQ0FFFRURgwYAC2bt1qdd3GjRuRlpYGvV6PVq1aYeTIkTh69Khg+yZPngyj0ciZ637t2rUIDg7G/fffD6CloBcA3mrOWq30t9uePXug0WiwYcMGzJs3D+3atUNERASGDx+OX3/91e76rKwsDBs2DJGRkQgLC0NSUhIWLVpkdc3WrVvNxa3Cw8MxYsQI5ObmWl2zYMECaDQaHD9+HH/5y18QGRmJmJgYzJo1C01NTfj1118xatQohIeHIyEhAS+//LJdW6qrq/HMM88gMTERQUFBaN++PWbMmCFqxZ74H+oLXCfm3uYL+2PveUuffvopbr75ZnN/0rlzZzz22GNW18hxn69ZswZt27bF+++/77D6+v79+5GWloaQkBC0b98e8+fPx7vvvguNRmNXSM/ZvzMhhPgSmuh42enTpwEArVu3Nj/W0NCA8ePHIyMjA1u2bHFY+OuRRx7B9OnT0a9fP2zcuBEff/wxxo8fb/Wh99JLL2HixIlITk7GJ598gg8//BCXLl3CoEGDUFBQ4LB9EydORFhYmN0Hb0VFBbZs2YI77rgD0dHRAICbbroJOp0O06dPx0cffYSSkhKpvw5ec+fOxZkzZ/Duu+/i7bffxqlTpzBu3Dg0Nzebr1m9ejUyMzNhMpmwatUqbNu2DU8//TT++OMP8zXr16/HhAkTEBERgQ0bNmD16tWoqKjArbfeiv3799t933vuuQe9evXCpk2bMGXKFCxduhQzZ87E7bffjjFjxmDz5s3IyMjA7Nmz8fnnn5ufV1tbiyFDhuD999/H008/jS+//BKzZ8/Ge++9h/Hjx1tVFiYEoL6AC8MwaGpqsvvPZDLZXSv13haSm5uLe++9F507d8bHH3+MHTt24N///jeamprM18hxn3/33Xc4ceIEHnroIcTGxuKuu+5CTk4OioqKrK47fvw4RowYgdraWrz//vtYtWoVjhw5ghdffNHuNV35OxNCiE/x8o6S32DDVQ4ePMg0NjYyly5dYrZv3860bt2aCQ8PZ4xGI8MwDPPwww/zhnw8/PDDTKdOncz/3rdvHwOAmTdvHu/3PXv2LBMYGMg89dRTVo9funSJMRgMzD333CPY9ocffpjR6XTMhQsXzI+98cYbDABm165dVteuXr2aadWqFQOAAcDEx8czDz30ELNv3z7B78Mw9uEqu3fvZgAwmZmZVtd98sknDAAmNzfX/PNEREQwAwcOZEwmE+drNzc3M+3atWN69OhhFTpz6dIlpk2bNswtt9xifuz5559nADD/+9//rF4jNTWVAcB8/vnn5scaGxuZ1q1bM3feeaf5sUWLFjFardYuPOmzzz5jADA7d+4U9fsgvof6AvF9gdB/bOialHvb9nfHYu951quvvsoAYCorK3nbKMd9/thjjzEAmBMnTjAMc63Pmz9/vtV1f/nLXxi9Xs/8+eef5seam5uZ5ORkBgBTVFTEMIw8f2dCCPEVqtrR2bdvH8aNG4d27dpBo9Hgiy++kPwaDMPg1VdfxQ033IDg4GB06NABL730kvyN5TFgwADodDqEh4dj7NixMBgM+PLLL9G2bVur6+666y7B12JDSKZOncp7zVdffYWmpiY89NBDViuiISEhGDJkiKisRZMnT0ZjYyM+/PBD82Nr165Fp06dzKE0rMceewx//PEH1q9fj6effhodOnTAunXrMGTIELzyyiuC34vP+PHjrf7ds2dPANfCfL777jtUV1fjySeftAs/Yf366684f/48HnzwQavQmVatWuGuu+7CwYMHUVtba/WcsWPHWv07KSkJGo0Go0ePNj8WGBiI66+/3twWANi+fTtSUlKQmppq9XsfOXKkz2WLUhul9CPUFwi755578MMPP9j9t2TJEqvrnLm3hfTr18/chk8++YQzI56r9/nly5fxySef4JZbbsGNN94IABgyZAi6dOmC9957z2rnau/evcjIyEBcXJz5Ma1Wi3vuucfqNeX4OxPHlNKHEEKEBXq7AVLU1NSgV69eePTRR0V9+HOZPn06vv76a7z66qvo0aMHqqqqUFpaKnNL+X3wwQdISkpCYGAg2rZtyxnDHhYWhoiICMHX+vPPPxEQEACDwcB7zYULFwBc+9C2JSZWftCgQbjhhhuwdu1a/OMf/8Dx48dx5MgRzph2AIiMjMTEiRMxceJEAMDPP/+M4cOHY968eZgyZQqioqIEv6et2NhYq38HBwcDAK5cuQKg5XcBANdddx3vazg6N9CuXTuYTCZUVFQgLCzM/HhMTIzVdUFBQQgLC0NISIjd49XV1eZ/X7hwAadPn4ZOp+Nsiyffc8SaUvoR6guE+4LWrVvjpptusnvc9jyKM/e2kMGDB+OLL77A66+/joceegj19fXo3r075s2bZ/55XL3PN27ciMuXL+Oee+5BZWWl+fF77rkHixYtwq5duzBy5Ejzz2g7CQZg95gcf2fimFL6EEKIMFVNdEaPHm21km6roaEBzz33HD766CNUVlYiJSUFS5Yswa233goAOHHiBFauXIn8/Hx069bNQ622lpSUxPnBbYlvR8JW69at0dzcDKPRyHvol139++yzz9CpUydpjbXw2GOP4dlnn8WhQ4ewfv16aLVaPPLII6Ke2717d9x3331YtmwZTp48if79+zvdDj7suQbL8zi22MkS13mB8+fPQ6vVms8YuCouLs7hoWLLVVniWUrpR6gvkK8vkHJvh4SEoL6+3u46rkHmhAkTMGHCBNTX1+PgwYNYtGgRJk2ahISEBKSlpbl8n69evRoAMGPGDMyYMYPz6+xEJzY21jyJsWQ0Gjm/p6t/Z8JPKX0IIUSYTy3tPProozhw4AA+/vhjc7asUaNG4dSpUwCAbdu2oXPnzti+fTsSExORkJCAv/71rygvL/dyy53DdrQrV67kvWbkyJEIDAxEYWEhbrrpJs7/xHj44YcRGBiIt956Cx999BGGDRtm9yFaVlaGhoYGzuezhQbbtWsn6vtJdcsttyAyMhKrVq3iPQDcrVs3tG/fHuvXr7e6pqamBps2bTJna5LD2LFjUVhYiNjYWM7fuSeLPRJp1NiP+HtfIOXeTkhIwMWLF60mDQ0NDfjqq694Xz84OBhDhgwxh8yx2ctcuc9PnDiB3Nxc3HXXXdi9e7fdf8OGDcOWLVvMu1VDhgxBTk6O1YTMZDLh008/tXpduf7OxHlq7EMI8VWq2tFxpLCwEBs2bMAff/xh/gB95plnkJWVhbVr1+Kll17Cb7/9hjNnzuDTTz/FBx98gObmZsycORN33303cnJyvPwTSDdo0CA8+OCD+O9//4sLFy5g7NixCA4OxtGjRxEWFoannnoKCQkJ+M9//oN58+bht99+w6hRoxAdHY0LFy7g0KFD0Ov1DjM5sQwGAzIzM7F27VowDGMuIGhp9+7dmD59Ou6//37ccsstiI2NxcWLF7FhwwZkZWXhoYcechha5opWrVrhf//7H/76179i+PDhmDJlCtq2bYvTp0/j2LFjWL58ObRaLV5++WXcf//9GDt2LP7+97+jvr4er7zyCiorK7F48WLZ2jNjxgxs2rQJgwcPxsyZM9GzZ0+YTCacPXsWX3/9Nf7xj3/g5ptvlu37EXmotR/x975Ayr1977334t///jfuu+8+/POf/0RdXR1ef/11qwyOAPDvf/8bf/zxB4YNG4brrrsOlZWVeO2116DT6TBkyBAArt3n7G7Ov/71L86drUuXLuGbb77BunXrMH36dMybNw/btm3DsGHDMG/ePISGhmLVqlXmNNZsSJpcf2fiHLX2IYT4LO/lQXANAGbz5s3mf7NZuPR6vdV/gYGB5iwzU6ZMYQAwv/76q/l5hw8fZgAwv/zyi1vby1ck0NbDDz/M6PV63q/ZZgtqbm5mli5dyqSkpDBBQUFMZGQkk5aWxmzbts3qui+++IIZOnQoExERwQQHBzOdOnVi7r77biY7O1v0z7BlyxYGABMTE8PU1dXZff33339nnnvuOSY9PZ0xGAxMYGAgEx4eztx8883MG2+8wTQ1NQl+D/BkXfv000+trisqKmIAMGvXrrV6fOfOncyQIUMYvV7PhIWFMcnJycySJUvsfhc333wzExISwuj1embYsGHMgQMHrK5hMzBZZjhiGP6/z5AhQ5ju3btbPXb58mXmueeeY7p162b+2/To0YOZOXOmObMW8S5v9CPUFzjXF1jiKxgq5t5mmJZ+IjU1lQkNDWU6d+7MLF++3C7r2vbt25nRo0cz7du3Z4KCgpg2bdowmZmZzLfffmv1Ws7c5w0NDUybNm2Y1NRU3p+/qamJue6665gePXqYH/v222+Zm2++mQkODmYMBgPzz3/+k1myZAlndjg5/s5EmNrGIoT4Gw3DqLOgh0ajwebNm3H77bcDaDnUef/99+Pnn39GQECA1bWtWrWCwWDA888/j5deegmNjY3mr125cgVhYWH4+uuvMWLECE/+CIQQL6N+hKjdbbfdhuLiYpw8edLbTfFL1IcQomw+E7rWu3dvNDc34+LFixg0aBDnNenp6WhqakJhYSG6dOkCAOYPBzq0SQihfoQo2axZs9C7d2906NAB5eXl+Oijj7Br1y5zGBzxPupDCFEWVe3oXL582Vw9vHfv3vi///s/DB06FDExMejYsSMeeOABHDhwAP/73//Qu3dvlJaWIicnBz169EBmZiZMJhP69euHVq1aYdmyZTCZTJg6dSoiIiLw9ddfe/mnI4R4AvUjRK2mT5+OrVu3wmg0QqPRIDk5GTNmzMADDzzg7ab5FepDCFER70bOScOe17D97+GHH2YYpiXu+d///jeTkJDA6HQ6xmAwMHfccQdz/Phx82ucO3eOufPOO5lWrVoxbdu2ZR555BGmrKzMSz8RIcTTqB8hhLiC+hBC1ENVOzqEEEIIIYQQIoZP1dEhhBBCCCGEEIAmOoQQQgghhBAfpIqsayaTCefPn0d4eDg0Go23m0OIX2MYBpcuXUK7du3MRQqVjvoQQpRDjX0IQP0IIUoith9RxUTn/Pnz6NChg7ebQQix8Pvvv8ta3d6dqA8hRHnU1IcA1I8QokRC/YgqJjrh4eEAWn6YiIgIL7eGEP9WXV2NDh06mO9LNaA+hBDlUGMfAlA/QoiSiO1HVDHRYbeIIyIiqHMhRCHUFLpBfQghyqOmPgSgfoQQJRLqR9QTHEsIIYQQQgghItFEhxBCCCGEEOJzaKJDCCGEEEII8Tk00SGEEEIIIYT4HJroEEIIIYQQQnyOpInOokWL0K9fP4SHh6NNmza4/fbb8euvvzp8zp49e6DRaOz+++WXX1xqOCFEnagfIYS4gvoQQohYkiY6e/fuxdSpU3Hw4EHs2rULTU1NuO2221BTUyP43F9//RUlJSXm/7p27ep0owkh6kX9CCHEFdSHEELEklRHJysry+rfa9euRZs2bXD48GEMHjzY4XPbtGmDqKgoyQ0khPgW6kcIIa6gPoQQIpZLZ3SqqqoAADExMYLX9u7dG/Hx8Rg2bBh2797t8Nr6+npUV1db/UeImjWbGOQWlmFL3jnkFpah2cR4u0mK4Y5+hPoQokTUD7gHjUUIkZ+v9FeSdnQsMQyDWbNmYeDAgUhJSeG9Lj4+Hm+//Tb69u2L+vp6fPjhhxg2bBj27NnDu/KyaNEivPDCC842jRBFycovwQvbClBSVWd+LD4yBM+PS8aolHgvtsz73NWPUB9ClIb6AfegsQgh8vOl/krDMIxTU7SpU6dix44d2L9/P6677jpJzx03bhw0Gg22bt3K+fX6+nrU19eb/11dXY0OHTqgqqoKERERzjSXEK/Iyi/BE+uOwPYm01z935UP9FFdp1FdXY3IyEhZ7kd39SPUhxAl8cV+wBVq6EMA6keIf1JLfyW2H3EqdO2pp57C1q1bsXv3bskdCwAMGDAAp06d4v16cHAwIiIirP4jRG2aTQxe2FZg11kAMD/2wrYC1W4Hu8qd/Qj1IUQpqB9wHxqLECIvX+yvJE10GIbBtGnT8PnnnyMnJweJiYlOfdOjR48iPt77s0FC3OlQUbnVtq8tBkBJVR0OFZV7rlEKQP0I8SfUD8iP+hBC3MMX+ytJZ3SmTp2K9evXY8uWLQgPD4fRaAQAREZGIjQ0FAAwZ84cnDt3Dh988AEAYNmyZUhISED37t3R0NCAdevWYdOmTdi0aZPMPwohynLxEn9n4cx1voL6EeJPqB+QH/UhhLiHL/ZXkiY6K1euBADceuutVo+vXbsWjzzyCACgpKQEZ8+eNX+toaEBzzzzDM6dO4fQ0FB0794dO3bsQGZmpmstJ0Th2oSHyHqdr6B+hPgT6gfkR30IIe7hi/2V08kIPEnOg4uEeEqzicHAJTkwVtVxxrtqABgiQ7B/dgYCtBqOK5RJjfejGttMfIOv9gOuUOv9qNZ2EyKWmvortyYjIIQIC9Bq8Py4ZADXspWw2H8/Py7Z650FIcR9qB8ghKiFL/ZXNNEhxI1GpcRj5QN9YIi03uY1RIYoJkUjIcS9qB8ghKiFr/VXThcMJYSIMyolHiOSDThUVI6Ll+rQJjwE/RNjVLUiQghxDfUDhBC18KX+iiY6hHhAgFaDtC6x3m4GIcSLqB8ghKiFr/RXFLpGCCGEEEII8Tm0o0OIj2g2MT6xzUyIP6D7lRB1oHtV3WiiQ4gPyMovwQvbCqwqGsdHhuD5ccmqOzhIiK+j+5UQdaB7Vf0odI0QlcvKL8ET645YdcQAYKyqwxPrjiArv8RLLSOE2KL7lRB1oHvVN9BEhxAVazYxeGFbAWdhL/axF7YVoNmk+LrAhPg8ul8JUQe6V30HTXQIUbFDReV2q02WGAAlVXU4VFTuuUYRQjjR/UqIOtC96jvojA4hIij1MOLFS/wdsdjrlPqzEaIkctwnctyvYtA9TYhrPHWv8qF7WD400SFEgJIPI7YJDxG+yMF1Sv7ZCFEKue4TV+9XMeieJsR1xaU1oq5z5V7lQ/ewvCh0jRAHPH0YsdnEILewDFvyziG3sEww/rd/YgziI0PAt86jQUsH2T8xxu5rdNCSEGFy3idC9ysAxOqD0LdTtNfbSog/4PrMzcovwdLsUw6f5+iz1RV0D8uPdnQI4SF0GFGDlsOII5INsmwpO7OKE6DV4PlxyXhi3RFoAKu2si16flyyXfs8/bMRokZy3yeO7ldWWU0DhryyW/LqLd3ThEjD9ZlriAhBXVOzqOdzfba6gu5h96AdHUJ4yH0Y0dFujSurOKNS4rHygT4wRFpvoRsiQ7DygT6cgyU6aEn8hdRdUkvuuE/47ldLzqze0j1NiHi8n7nVdaisbRR8/ozhN8geRkb3sHvQjg4hPOQ8jOhot2ZEssHlVZxRKfEYkWwQfXjR2wctCfEEV2Pd3XWfjEqJR8aNbTFgUTbKa+wHVex9v2DrzwgP0aH0cj3d04S4wPJwf1yrYCzY+jPnZ65YCXFhsrWNRfewe9BEhxAexaW1oq4TOozIrhzZdqrsqu2M4V1Fr+KkdYnlvS5Aq3H4dSltlnodIUojdN/x7XZacud9cvhMBeckh8UAMFbX4/53vzc/5miSRvc0Idy4Fjxc5Y77iO5h96DQNUI4ZOWXYFn2SYfXiDmMKKbo2NoDxaLadPFSnUthOJZcSWJAiNLJVeyvb6doxOh1vF935T5xZlXWUUgb3dPEn/F9NvKFqLnCXfcR3cPuQTs6hNhwNEiyxMDxYcRmE4P3DhQJ7tZUXhGOBwZadpgGLsmRJeWks0kMCFEDKbHufLug7Cqwo10XwPn7xJlVWUehrHRPE3/FF6I6f0wSFu444VKIGpf7+nV0y33kjnuY6vHQjg4hdoQGSayZw7vyTjCy8kswcEkOFu44Iep7RoXqHK7iRIXpsCz7pKwpJ51JYkCIGrga6y5mFVirAf42ONHp+0RMqmkujg4k0z1N/I2jRD5Prj8q604Oyx3nc1hy3sPsOGTiOwcx/eM8THznIAYuyfG7FNW0o0OIDbGDpIQ4PefjfGcDHHk0PRHLsk9yruKw/3ZHykmpSQwIUQNXYt3F7uiaGODtfUXo3THabvAhZhVVTKppR/j6Kbqnib8QE6LqDqWX6rEl75zb7i057mE5zij6CproEGLDmUESO7AxVl2RvFUeHxmCaRnXo5uhlX1O/8gQ3Nevg8PiZWKTFfCRksSAEDVgd0uMVXWc96IGLfcWV6y72B1dlu0ig5RMb+zqrTMHpU9duITcwjLeSRTd08TXSb1XxRBadNBqYBWp4Wz4uBBX7mGqx2ONJjqE2JA6SHI1o8tNnaIA8K/ibD9+XtTrUMpJQlq4Eusu5T5iFxneO1CER9ITsavAKHkV1fa+j9MH4x+fHsOFau7+h7V8dyGW7y5020CLEKVz9TOP/Sy/56br8P53Z1B5pVHUTq4lZ3dI3Hl2Ro4zir6EJjpE8Tx9mE7KIMmZMDVb244b8UNxDhaMbxms2HY8lHKSEOn4dksMAhMDZ+6jhTtOYPnu06hvMjm1imq7ertgvPiQNn8MRSEEkHav8n2Wj+8Vj9e/OS14n2k19pMcwLkdElfrewlRaz0ed431aKJDFM3dHQIfMYMksbH8YhirrQcrVsXN9MEwRITwrvA6CsMhxJ/x7ZICQG5hGecHqtCOLp8KgWrqUlZRpYS0+WMoCiGA+OiL+WOSsXCH9b0UGabDI2kJ+PiHsw7v86gwHabeej1e3MmfWEjKve2JszNqXBx151iPJjpEsbx9mE7oQKA74oNf2FYAkwl2nXJUmM48oLH9fTAA5o9JogEOUSxvpji13S0R+kB1NUmAELGrqJb9z4HTpVi++zTvtf4WikIIID76YlRKPEamGLA85xTWHihG5ZVGVNY2Ytk3/GdfWZW1jagSWQJC6N721NkZV84oeoO7x3qUXpooklwF/1zFDpImpLZHWpdYq85H7m1fdrDy5Hr7VJlVV1eLI8O4ixcu3HHC71JGEnVQUopTR6loLdO086V4lYOUVVS2/+natpWo65UWikKIu4lNx7yrwIhl2adE162zJm6cIXRvSzk74wp2AgjALn290mpqeWKsRzs6RJGUeJjOdlU6Th/ske8LXFvtAcN9s1OcPlEiOVbq5NoNkrqaarmjkl1gxOa8c4LFQx3RAGgbEQwTw0hOTavGUBRCXCHlvheKvnA1zDytcxw2HTnn8g6JJ8/OOHtG0dM8MdajiQ5RJKUdpuMKdzFEhCAqTIeqWv5MLTF6HSb0ao+13xW73AYGQOWVJt6vUZw+URI5wjSkxm07Ghw584HK7qikdYnF3DHJeO9AkegiwJbYsJq6JhPuf/d7UT+LJbWFohDiCmfOazhKx+xKmLkGQNWVRqezOFqSc8FCzERQDTW1PDHWo4kOUSR3rWBKXR1uNjFYnnMaS7NP2n3NMjkAX+f30h09MColHkGBGrzzbRFn1ha5UJw+URJXV+qk7gYJDY7EflAaq+usEhX07RSNw2cqcPFSHW6Mj4AhIhgXquslrQ5HhulQWdtyLsBSSVUdHl93BDOHd8W0jK68fZEr6bIJURN3nNdwZZDMAJi6vuX7itkhcTTGEJPoJEavQ99O0Q7bJGUiaDkB9OZZST6e2K2miQ5RJHesYEpdJcrKL8GCrT/DWF3P+XrsqnRUmA7BgVqr6yw7v6z8Ery9r8itlZotUZw+UQJXVuqk7gYJDY5WTOqD0kvc97Gthdt/tgpRs00ryyYGESMqTIc37u2Nf246DoA/7G1p9ilsOPS7OcU8F7WEohDiLHcd1pcjpPOFbQXYPzsDGTe2xYe5xThTXotOMWF4MC0BQYEtx935xhjzxyQhWh+Mi5fqzAXA+RKdlNc0Ysgrux2OS5yZCHorg60QT+xW00SHKJLcK5jOrA6LqY/DoCWt7Ed/vRlajcZupUTOFNRiUZw+UQJXVuqk7Ab1T4wRPMw6bcMR0buptudwbJ/HJgaJurpLw4XtlRbf2QOBgVoYq4UnfbYp5rmoIRSFEGe567yGsynjbb/v8pxT+PiH363a+O7+IvPBf64xQ0uCoaNWj0VdTSrE13/wjUucnQh6O4OtI57Yraasa0SxxGZTESI1q4czk5PSy/Wc2dnkTkEdFaazy6LC0qBlhYbi9IkSsIMLZ96vUnaDxNxjQpMcKR+h7IAiVBeAjybfjMfSExCjD7K6xrKPkrrDKpRhyFEmSELUzF3nNRxlIZNiafYp3oyNz37+k+gxQ1VtI6pqG9EqmHuvgS/bmDNZ25SSwdYRucZ6fGhHhyiaHCuYUleJnJmclF6qR7OJsWuXXGFkESGBePnungBAcfpEFVxZqZOyGyTHPRYeEojqOu5EH1zYPkOr1eDf47pj3phk3j5KSnZGOmdH/Jk7z2tIKcIrBduv8e3OOHrO5Xr+PoerL3BmIih2/LN0169Iv76113aI3blbTRMdoniOsqmIIbVzcGbgtHDHCazYU4j/TkhBZs9rqw9yhZE9P/ZaHC3F6RO1cPZciZS4bVdrTgCQNMmxxPYVDvsoJz6n6Zwd8UfuPq/BNZiuqGnAf7YXiAov9QbLvsCZiaDYvmT57kIs313o1XM7ro71+NBEh/g8qZ2Ds5OT8poGPLn+CMblGzA82WDO2ORKbDCrXXSY+f9TnD5RE2fer1J2g1yNv3eFmL6i9LK4JAiOXleJ2ZIIkZsnzmtwDaZHphiwPOcUlmafsrueL2mAp1j2BX07RSNGr+Ot58U1EZQ6nlHCuR250USH+Dypq0Ts9c5ub287bsS240YALWcQxveKx9v7ipzuMDUAKmoarB5z18oHIe7gzPtV7G6Q5eDIk8Seh5My0OAaqCg1WxIh7uCN7IIBWg2mZXQFAKw9UIzKK9cmEobIENzXryNniQl3su0L2H7A0SQHsJ8ISl0I8sWafDTRIT5P6ipRgFaD8b3i8da+Ipe/t7GqDm/vK8LfBidi67ESq47bNm0tH3Mef63vrLAQIoaU3aDQoADUNjR7rG3zxySJGgSIHWhw9UVKzpZEiLt4OmqBazEhKlSHR9MTMS3jegDAxz+c9eiuMYNrfYGYLLB8E0FH4x9H39uXzgrSRIcomlwhG1JWidi6N3JgV0e2HivB3n8ONRcebIkNrrdLO+nodXxphYUQsYR2g7LyS/C4h3dzACBaHyyqfxK74xQVpsOiO3tYFR50R00RQtTAU1ELfJOIqiuNWJZ9Et0MrTAqJV70rrHYBUwhM4d3xaiUeFFZYGP1Qdj7z6Hmej62RiQbMGN4V7vdKiFf5pcAgOpDZWmiQxTLlZANrgGImFUid9S9YVdHPswtxiPpiVbfb/KZCqw+UCzqdXxphYX4hmYTg4OFZcj9rRRAy8BkQGfPpTxm71dv2FVgxKxP8kT1TyOSDRjb02AOaeVSYZO1yV01RQghLaQsJoxINuCuPtfhsyN/OHxNubI0/3y+GgdOlwIMBMPoy2oacPhMBWc/wDWOahUcgMv1wrvfH+SewQe5Z1QfKksTHaJIQiEbM4bfgIS4MM7JitAEydGgQO66N5YW7jhhLi7GdhjDkw2iJzoA8Nbe0wDUv8JC1C8rvwTPfv6TVVrV5btPIypMh8UWOxPu5I77tVWwFpfrTYLXreG4b7lCyrh+T3zYQRUAHDj9p6j2UoY2QqyJjQQRu5iwPOc0Pv7hrNvGBly+LriArwsuQB8UIOr6i5fq7H7uipoGTF1vP46qETHJsaT2UFma6BDFEVPgyvJgoOUkhm+CVFJVh8fXHcHM4TdgWsb1vJMEdw8ajOZ2dEVCnB5xrYJhiAiGsVpcZqY9J0ux52Sp6ldYiLo5CherrG3E4+uOYJUHPhTdcb+KmeTwhafYrgLvKjAKxtZbcmZQJVcKe0J8gZRIELH9h6cTEViqEXnusLi0BgOX5NidA3Y0jhJL7aGyNNEhiiN1lZZdbVgxqTcW7jjh8CZemn0SGw6dwYLx3a3C2OJaBQMMcOrCJdd/AAeuTdSupbGMCtNJfh21r7AQ9Wo2MViw9WfB69z1oWgZLneu4oqsry2EPczrKDyFXQU+WFjmVBis2EGVqzVFCPE1UpN3+MoigT4ogDM1tlxhdIC6Q2VpokMUR+oqLbva8NyWfN7Ui5aM1fV4fN0RRIXpJFUzdhdn2qD2FRaiXoeKykXtQLrjQ1FKGJg7GCJDkJkiLtw097dSt4W6yFVThBBf4UzyDm/W4JKT2F0fOagxVJY7RQMhXuTMKgsDiJrkWFLCJMcVlisshHiKlA86OT8U2XA5V+/bWH0Qnry1s+TnzR+ThP2zM5CR1FbU9e4cOBkiQ2g3lxALUpJ3sNiMiMC1xQMWLR9wU+MuGE10iOKwqyzU0YijxhUWol5SPujk+lAUGy4nRllNA7Qa6b1LXHgwAKDgfJWo66NCgyR/DzGmDb0e+2dn0CSHEAtiPwdtr2NLTxgirfsqQ2QIZg7vKlv71E4D8UWSgZY+O7ewDFvyziG3sAzNcsbRSUSha0RxnClw5S1je8Rj+08lXm2DGldYiHr1T4wRlUBDyoeiELHhcuJJn+hwHfZ1JK5VkFvCYtKvj+MNV5Or7hghaiP2c5DrulEp8ci4sS0+zC3GmfJadIoJw4NpCQjQavDxD787dQ9Hheok1axRMqmhsjuPl1w9StBgfsybCZRookMUaVRKPP42OBHvfFsExssznYwbW+NEySWrAU6sPgh39mmPbcfOe61ddBiZeEOAVoMF47sLFumU8/yI3LuWgVqN6EmIBi0JQ7gO+zpiiAzF8+OSZS1mGh2m473fubJNxeh1+O+EFGT2bCdbGwhxlTsm5ELnbRx9XnLdO+98+xsm9u+I0SkGrDlQLHnRdcX9fQAGmLr+iNcnPK4uGHMVVuezaGcB3uIouF7ixQRKNNEhipSVX4K39xUpYjcn55c/8eak3ojWB1vkp6/H1PVHvdY+OoxMvGlUSjxWPdCHMzFAdJgOi2SuoyP3ruV73xXhpTt6YOr6ow4HAeydJeU+txxQvZx1QtLzhL5PRW0jdhUY7X63fNmmymsa8eT6o/j7H5WYk5ksui2EuIsrhcAdcRQJ4ujzkjdTW3W91eKGRgNRi67s/c8WTl58Vw88cXWxw1vjBan9V9uIYPzvnlSUXq6XNBHdefw85yTHsh3eSKBEZ3SI4jjKnuINGrQU++yfGIMJqe3RPzFGMI21rTCdFmN7xkMD6UEzYUEBiNFbx/vTYWTibaNS4nH4uRH4aPLNmDa0C6YNvR4f/fVm/PjcCNnfl2y4nBCxn52VV5oQGRbEGZtvyRAZghnDbxCdAMFyQPVVfonDD32u7/XmpD4O082zmaMs493F9Jdv7SvCzuPeDbElhJ1U2IZ/sumfs/L536PNJgYHTpfi1a9+xatf/YIDp0rtzn04Om/D9XkpZazBfqvJ6QmYOfwGzs9yrgkVX5vEiNHr8Po9qaL7NVex32bB+O5Ivz4OE1LbI61LrKhJSbOJwXNb8gWv80YCJdrRIYojto7Obclt8XXBBbe3h83WsnTXSaRfHweTiZGcNrauyYTMlHiM7Rlvt5rVKjgQl+ub7J7Ddi3/d08vzvjhoEBapyDeFaDVIL1rHNK7xvFeI0eYithwuRBdAGpFplr97Mff8eo9qdb1tPTBgAbmlcy+naLx+jfiiwWyIR4jkg3o9+IuUc+ZNvR6pF8fh/6JMThUVO5wUsVVy0Jsfzl/Sz5GplAqeuIdzqR/ZnGllV++uxBRYTosvrOH1T3cJjwEe/85FIfPVAj2OVJr9mkA7Mw3Yv/sDHRt08ruHApfiNeolHhzGw+cLsXy3acFv9f8MUl4JD0RAPD1Lxew3QMLFVJC1GwdKioXnfnW0wmUaKJDFEfsTXBD21Yemeiwlu8+jeW7TyPYiQmGiQGeXN9SLX7/7AwcKipHdoERm/PO8XYOkWE6PHpLIkwmYMgru6065Hf3F3ntYB8hYskZpuIoXI4ldpIDAJvzzuNgUTlvW7LyS+zuO0fYgUmAVoPcwjIJ6e4ZXLzUMnkxVokrgGrZR4rtL8tqGlRZ7I/4Binpny3fo2xaeS6VtY2cNfHYPmZCanuHCy3O1OwrqarD69+cxCc//mE1yYnR6zB/TBJvvxag1SCtS6zo73mmvBbLc05jw6GzMFa7f2Jg2X85Q8rv0tMJlGiiQxRH7E2Q1jkOm46c83ixr/omk9PPffbznxAerEPVlQasOVDssN2VtY28VdJtKz1TtiWiNFKrlDvCvr/rm0y4pXMMdubLs8BRUlWHx9cdwZuT+mBkyrVV4eLSWizLPim6X4mPDLEaJEj50F++u9D8/21DVPlY9pFSBg2Uip54Q0vY2Z+irrV8j7aklS8QfI7twgfbx/xtcCK2HivhXWhxdsD92jf2OzIVNY2Yuv4oVmo1Dvu1OL1wCC4AfJB7xqm2ScWeKXJlkgOI74di9UEeT6BEEx2iOH07RSNGr+NdETUf9usSq5o01KzK2kbcv/p7aDWutddyq99kAhbukP9wJyHOciVMxRbXrpDcpm44goiQQFRdsQ8hFWN8r3irn8PZAVSFxQoxF67MUf0TYxz2l5YoFT3xNKn3r+V7tCWtvPT7nu13uM7IWS60jEg2ID4yRJa+hf2eczf/hCuNJhgiri04sgs1uwqM+CLPu5laxSZpkIrNeif0u1w4IcXji7CSYnAWLVqEfv36ITw8HG3atMHtt9+OX3/9VfB5e/fuRd++fRESEoLOnTtj1apVTjeY+DY2XMTRJAe4dmOyB/3aRqjrA1yO2lnsNvqT65073Okt1I/4PmeqlHPhO7wsN4aB05McoGVAZXmvsR/6ktvh4Gt8g5IArQb/nZAi+Npy1jXyNupD1EHK/ctVkNIdO5DsPfbCtpadovG95F0MLK9pxMyNeZj4zkEMXJKDRTsLMHBJDia+cxBrDhRbhbt50vCkNqKTNDiDzXrnaArz98GJyOzp+cVXSROdvXv3YurUqTh48CB27dqFpqYm3HbbbaipqeF9TlFRETIzMzFo0CAcPXoUc+fOxdNPP41Nmza53HjiW8R0ilw35qiUePzvL7080UTVsOzMvVmRmAv1I75PylkTvgraSsu+KGTO5z+Z285+6MvJ0aAks2c7/H1wIu9zNfCtVPTUhyifM/ev7Xs0rpW4MC+p2IWWg4Vl2HrMfYuBJVV1eGtfkdsXasTI+eUi5o2+ERumDMBr96Viw5QB2D87Q9aoD3bh2XaRJ0avw5uTenstxb2k0LWsrCyrf69duxZt2rTB4cOHMXjwYM7nrFq1Ch07dsSyZcsAAElJSfjxxx/x6quv4q677nKu1cTniOkUY/VB2PvPoZzZxij23B7f4U5vo37E91ieESsurcF73xWLel5xaS0GLsnhDLuMDA1SxABBrIraRhz8rQzp17dkoBuRbLA7JO0sR30fa05mMnpdF62oiuTuQn2I8knNaPa3wYn271E3r3Lk/laqqj7GFSYGmPZxHlY90AcTUtu77ftYZphTyplhl87oVFVVAQBiYvi3w3Nzc3HbbbdZPTZy5EisXr0ajY2N0OnsawbU19ejvr7e/O/q6mpXmklUQEynWFbTgMNnKjgH7d7aDlYDpU8C3dGPUB/iOc6codGgJasg14F/NuzysfQEOZvpEbmF1yY6QqmipXDU91nK7BmP4cn+l4qexiLKI/VzZ+uxEvxrVJLVgLi0pt7BM+TgGzucUniiYCebYU4pnO79GIbBrFmzMHDgQKSk8McHG41GtG3b1uqxtm3boqmpCaWlpZzPWbRoESIjI83/dejQwdlmEpUQ2ynyXRfjpi1uX6DkA8ju6keoD5EXX3iZs2doGACNTSbeZAUAsPHH311qs3dc+4nkXmAQ83rsGceFO07gg9wzWLjjBIa8sluRZ/XkQmMRZZL6uVNSVYflOadceg0p4iNDFDUY9xShs5F8fb2aOb2jM23aNBw/fhz79+8XvFajsZ45MgzD+Thrzpw5mDVrlvnf1dXV1MH4OLEdWpvwEM5UygaVJSOQw5RBidh+vIQ3vTZXhialcVc/Qn2IfPhq4cwfk4SFO05Iji6J0euQ3iUW244bea9hAFyuF18TRynCQwJx4HQpSi/Xo/SSvKvRQn2knOm81YTGIsrEJuSQUv5hafYpdDOEm9+nzryGWON7xWNA51i3vb4UGgBhwQGo8VCfxy6a2I6lKmoafDKDq1MTnaeeegpbt27Fvn37cN111zm81mAwwGi0/kC7ePEiAgMDERvLPZsODg5GcDCt0Ps6y5ssrlUwDBHBuFBd73DQXlFTbxfTb4gIxr39OiAqVIfKK/KEiiidBsD24yWYPyYJU9cfdWvaSHdxZz9CfYg8HA2en1x/1KnXfC4zGf/ZLlwbQ40WfWmd+UurkSfDoiEi2OGChVA6bwB4dtNPCA/RYUDnWMX2CVLRWES52IQcT/AU++RjGVpl+Rpyl5B4a18Ryi43YlSKAWsPFHu1RAUDOJzkjOtpwPBkA+L0wfihuBxv7/sNtY3OT4rahIeIDjn2hYUSSRMdhmHw1FNPYfPmzdizZw8SE/mzvLDS0tKwbds2q8e+/vpr3HTTTZwxscQ/cN1kUWE6c40NrkH7+F7xmLr+qP2gq7qes4CXkmk1wKPpiVh7oMipgRCbaCBaH4yVD/Sx+11Ghunw6C2JGJFskK/RMqF+RPmaTQwOFpbh2U0/ORw8O6OitsFvFiTkivqY2L+jVT0O20O+Ys44Vl5pxP3vfu8TK7TUh6gDm4VrwdafYawWt8NZUlWH9w4UIS48GG3CQzAi2YC/DU7EO98WgZF5JvLZkT+u/cNLM52QQC1CggIcnuf78Uwllt3XBwFaDdK7xqFvx2g8uPaQ5O9luWDMNZbiIrXumRJJmuhMnToV69evx5YtWxAeHm5eHYmMjERoaCiAlq3ec+fO4YMPPgAAPP7441i+fDlmzZqFKVOmIDc3F6tXr8aGDRtk/lGIWvCtElddvdEjbXZmDJEhmD8mGQt3qCfVrBATA6zeb1/MTKqLl+owIbU9RiQbsDznFNYeKEbllUZU1jZiafZJfPzDWcUNaqgfUTZ3FejUAIjRB6GojD8FsLcptfBwQpyeN4Tw+XHJqG8yiX4tX1ihpT5EPdgsXMtzTmNp9klRz1m444T5/8uVuVCI3JMoseqaTKgTuH9ts6dqA6RPNthnODOWUmoGV7EkJSNYuXIlqqqqcOuttyI+Pt7838aNG83XlJSU4OzZs+Z/JyYmYufOndizZw9SU1OxcOFCvP7665TO0U+JCbGorrPu1GobmpDzy0W/SQMpBRu3v6vAiGXZp+xWypVYOJT6EeVyZ4FOBi3Zw9YdPCt4rVgaADzHK5yixEkOAHx7shSPc/xd2Pu7uLRW9GspucaWWNSHqEuAVoPpw7ti5vAbJD/XE5McNThw+k/z/Vp6Wfr5v6gwHVY+0AfReufT9is9gysfDcN4ax4rXnV1NSIjI1FVVYWIiAhvN4e4ILewDBPfOejtZqgeuwW9f3YGANidW+K7Vo5tZzXej2pss6c1mxiH7yNHlLoT4g/Y+5thGN4zjnw2TBnglRVatd6Pam23UjSbGKQv/kZ0GBuxZogIwcT+HdHYbMLy3dLC9Q0RwTjw7DBsP34e0z/Oc+r7e6u/4CP2fvTt5PpEcdS6IqA0DK4lGhCKz7fcdiaEj9QCfxq0hE69OakPDDaVsOMjQzBz+A1Yem8q9MEBMre0hVYDZNzY2i2vrSbs/T2xf0cA0iqDGKuu+FwqWaIctqmKAWDB+O4tO7HebZoqGavrsDT7pORJTstz63GoqNyplN1sX6/kDK6OuFQwlBApmk2M7ClXies1iIj/4TrULuX9we7g3NevAxpNJrz6l14A01Lgz/KQ/IHTpW5LmWpigJxf/nTLa6tRWU0DZgzvig2HzopeMV+444RVsWV2xTghLkwRFc2Jejk6U7ZiUm88tyUf5TXyhKXptBo00iRd0MVLdRjbs52klNq2GVz5EqIoGU10iEvEvunddcDZX1lmQZFSg4gQvgHIff06in6NyLCWLFVLs68V+GMHMZahDd8VchdilAvb09AQB/gg9wyAlsnK9GFd8f53xYLZ7SwnOcC1FWOWL2RoI57nKC394+uO2CUYCA8JxKW6Jqe/H01yxGkTHiI5ZbfBog9wNHlVch9BEx3iNLFver5OjziPDVd570ARYvRBiNEHoaKmQbWFQ4l7sQsS2QVGrD5QbPd1Y1UdlmWfRFSYDlW1jYL3KtcBYa5sXucqrsjQen7Up9i7UF2H1785hb8NTsTb+1oyOzr7e/KFDG3Es8QkHLLtP1yZ5JAW+qAA1DRw757bjgHYtN98haCj9cHm+obsTv1r2aewLPukKgsS00SHOEVsFW5HnR5xnWUaTi5qKBxK3EvMbipbK8GVpAK29RZ2FRjx9c9GoacRmbF/v63HSrBiUh+7Sucxep3okCFfqKFBPEvqWT8ij7/0vQ7vX93VFVM8nE37zReRk5Vfgmc+PSb4t1RDH0ETHSKZ0IqN5ZueOj3vMqhgW5m4j5TdVAZARW0j7u7THp8dOefU92N3GpfnnMKy7FO0wOFFJVV1OHXxEvbPzrAazBir6zBzY57o11F7DQ3iWXQW1Ds2HT2Hvw1OxNZjJVZjLkdjgACthvOelhqFo/Q+giY6RDIpWb6o0/O8GL0O88d2hyFCHQcFiXs4u5saFuz6x8LaA8U0yVGApdmn0M0QbjXIYbNfSUV9ORGDzoJ6x6W6Jry9rwgrJrXUynE2WYArUThK7SMovTSRTEqWL1c6PTkLAfqT8ppGGCJCkNYlliY5fszZ3dROMWEuf2+hQ/DkmimDEmCIcN/g0LYwaP/EGMRHhkhO70sDWCKGs+8vIo+FOwrQPzEGE1LbOzUGcCUKR6l9BE10iGRSsnw50+mxZwVWTOyNDVMG4LH0BLRyUy0OX6XUlRXiOVLfA2ythEk3d0KMXuf0940KE/dcvY4+fgDg0x/P4bH0BLe9vm0NLTbrEiCulonaa2gQz5L6/iLy4aqZZ1vLSKhWljNjB6X3EfRJQyQTmrxYvukddXrsv20HRobIEKx8oA9GpsTjUFE51hwoxmU31eLwVUpdWSGeI+U9wN6L43vFI+N/e1yqb/HoLYmirms0MTQQQsvu10tf/uLW78EOXthBT32TCTOG34C2AjtJlMyEOIPN6mVbSDj66mc9vZPci73fs/JLMHBJDia+cxDTP87DxHcOYuCSHGTll/A+V+rYQQ19BJ3RIZKxk5fH1x3h/DoD6zc9XypD9pAcV+aPXQVGpC/+RnThO9KCUkkTNpW0seqKw7TjlgyRIRjfKx5v7yty6WzNjGFd8cStXfDaNychVNqioZlO8UgVHKhBoFaDmgaTpOeVXqrHzuPnsXDHCes+OCIYM4d3RUKcHsWlNXbFRimZCXEWX1avXQVGu7GAVgPB/oKIV1xaI5gZd8bwGzgLA7ML2WILiqqhj6CJDvEIoVSG/RNjzF9bnnPKqhAhEUcNKyvEvcQW5mXTSD+WnoARyQb07RSNIa/sdjmBwMc//A6NRkODFjepb2JQf/WvFBWqwyO3JGDDoTO4cKnB4fP40tBfqK7HsuxTWPlAH0wffgOmZXRVXdVzolxcWb1sxwKll+oFyyQQaZZmn0JUmM5hLSO+wsCOCoqy/2YXR9TSR2gYhlH8R1J1dTUiIyNRVVWFiIgIbzfH7zWbGAxcksM7mGJ3FfbPzhB1A4gdnBHHPFWhWI33oxrbLJWUlKC275XcwjJMfOegy21wpQ4PkYbtWV0tDCq1v5aDWu9HtbbbEXYH2FuT2y155zD94zyPfT/Cb+bwrpiW0RUBWo3ogvDeJPZ+pB0dIpmU9NJCOdWl5msn9qLCdFgxsQ8GUJY1vyUmJaijtONyJa+g+9hz2JplW/LOY/qw6/Hed2ecynan9BoYxH08NZh1NJmi86TKsTT7FDYc+h0LxicLRuFw8fakmQ9NdIhkYgdFxqoryC0s433Tu5KvnVxb0V18Zw+kd43zaluId4lJCWqZdtwWDTbUiQFgrK7Hsm9Ou/xaX149oKyUwQlxL0dnOB5fd8Qc1sr3fhA7qBWaTPVPjEFUqI5S0iuEsbrlDM/KB/pgVEq86MUPJe8A0USHSCZ2ULRwxwmU11yLHbd907uSr52o4xAgucadq13GqisuXSf1ACrxPR/knsEHuWcUMzgh7uNokZF9bM2BYqw5UMz5fhA7qBU6EL/ygT4YkWzA8KS2+OzIH/L9gMRlL2wrwIhkg+jjB0J/Z2/2J5RemkgmtjaO5SQHuPamz8ovQbOJwYHTpe5rpI8LDwnA3n8OpcGISjiT5lMK23tN6nWWaeCJf7Psp4lvkrLIaPt+YAe1ts+3vU7MZOrZz39C+uIcmuQoDFc9Hj5i/s62RYs9jSY6RDJnC4Kxb/M5n/+E9MXfYPlu18Mt/NWlumYcPlPh7WYQEcQODFwR0yrY5evYNPBRoc4XCyXqp5TBCXEfKWfyLN8PDU0m0YNaMWd5K2sbYaymqA6lEvM+kXJm21tookOcwlcQrFVwgMPnMQAqahupPo4M5DpATtzHU6tdBoHCj2KvG5USjxWT+rjUFqJ+ShicEPeReiaPfT98mFsselArNpyWKJeY94nYcYg3xyt0Roc4zTYrR5w+GE+uPwKg2dtN8wt0gFz55MxQ6AgbTuroe8WLLCQ7oEssndchAGgxxVc5eybvTHmtqOuyC4zYnHfeucYRr5NSeFzsOMSb4xXa0SEuYQuCTUhtD61WgyrKnOJ2GggPWptNDHILy7Al7xxyC8soBMVLPLXaxYaTamAfTso+JqWQ7H39Ojg9yaHQN99Biym+ydnw804xYaKuW32gWPS5QaIsUguPC53ZFjNecTea6BDZ0Oqf+4nphPgOvu88XkKTHw/z5GoXXzipITJEdNYb9r2zNPsU59fFzJMeuSVBTHOJFxkighEVplP04IS4F19/wYV9PzyYliA4qKXM5Oom5fMCcDxpljppchcKXSOyodU/92sbEYwF47vzdkJ8aR5LququhhVeQ2lk3U8oRERKiIAYXEXe+naKxuEzFdiSd06w3oVQ8V4xc+O13xU5/wMQj/jfX1Jxqb4RT6w7Ag3sC70yAOaP8e7ghLifZX+RXWDE6gPFdtew74/RKQYcPlOBuZlJeGrDUd7rGJHrZ8GBWtQ3mVxoPZHLtKFd0LVtuNNlD9hJs23KcaWUwKCJDpGNmHMCxFWOqxJLKcCqlBz3voxd7eIaULprtYsNJwVaJi9DXtltV+9i/pgkROuDrSZDchXvrbrSJMOrEHcqranHhNT2nIMT1sIdBdBqQX2Dj2P7i7QuseiXGGP3ftBoWiYvbF0dDU9XZYgMwegUA9ZwTJa40CRHOdKvb211RtSZmm9ci2xKKT5MEx0imwCtBuN7xeOtfbSi6y62VYstSS3AyqBlsC2lMBiRzlurXY5396xXZGP0QRRT70fY3fdRKfEwmRi79wNACyH+iGuHx3YXl2/HZv6YJJy6WOP+RhJeITot6hrFTyC5IgrEFoPlYrnIpiQ00SGyaTYx2HqMisy5GwPuyYkzZ6TkyvpFHPP0apfU3T2a5PgPy7M3zSYGC3ec4LyOFkL8A9fqff/EGMz6JE/0a2gAzPsiHxW1lIzIm+oaTYgO04n6O3BFFPAtjql90YMmOsQllp1k6aV6ClvzEK7JiStnpCiRhPt5crVL6u4e8R/zRt9oHth4Kv05USa+1fv7+nWQHB1Akxxl6N0xCjm//Cl4ne15X6Gab2pe9KCJDnEaVydJPMd2cuJsbQSAEkn4Gpq4Ej4XLl0r1qyGYn/EPRyt3vNlXSTKl/d7lcgrNfjVeBn1TS1JakwM47OLHjTRIU4Rk6GJuJft5MTRwXc+cmf9IspAE1fCx7LooxqK/RH5Ca3eE/USG4ZsrK7D0uyT5n+LrX+mxkUPqqNDJJMa/0/co6Km3u4xqbURAO/nuCfy658Yg6gw5wp3xuip4Kcvsyz6qIZif0R+FNpKbFWKLPauxkUP2tEhklEnqQwLd5zAyJR4u0kK18H3ipp6LNxxQpE57okysLt7e/85FIfPVODA6T+xfHeht5tFZKTVAA+mJZj/7Y3058T73LEq3yo4EDX1TbQA6qPUHP1BEx0imRq3Ln2Ro3hZroPvI1PiFZnjnsjvUFE5KiUcDrYc1AYFatE/MQYHTpe6p3HEa6YMSkRQoHUgh9KL/RH5uWNVflDXOGTlG2V/XeJ9al/0oIkOkUyNW5e+SsqkU6k57on8pC5GROuDcHNiNPLPVeOXkkv4+IezMFbbh0YSddJqWiY5czKTOb+u5GJ/RH6uJK7h0zlOjxWTemPahqN2tXeIukWF6bDozh6qXfSgiQ6RzB2dJHEOTToJF7Hvi9EpBuw79SfKaxrwZf4FABfc2zDiFa1bBaN3x2iH19BCiP9wJnGNkI++P4NH0xNpkuODggO1GJFs8HYznEbJCIhkbCdJ3EvjYDHV8pBws4lBbmEZtuSdQ25hGZrpk8bviTlkHh2mw5f5RtTUN3uyacQLLl6qxxPrjiArnwo6kxZ8iWv0QQFOvV7llSZKS+2jjNX1OFRU7u1mOI12dIhTRqXEo0+nKBw+U+ntpvicqFAdHk1PRNc2rfDk+iOc1zBoiZfdVWDkLPhGsfX+hau6udAhc4ahCbG/UHvBP+Icrn7B8m9vG7IYpw/GPz49hpoGWvwg1tR8NpsmOn5IqPMTY+fxEprkuEmITotuhlaC1x09W4G39xVxFnx7Yt0RrHygD012/ABfdfPnxyXzHjK/r19HqxoKxPepueAfkc5Rv2D5uWAZsphbWAZjtXoHtMR91BwmTxMdPyO283Ok2cRg1id5bmohuVBdj8fXHRGsg/LOt/aTHIBWb/2Jo+rm7GR3/+wMu4WN7cfPe6W9xPsOnP6TEg34ODH9AtfnvZpX7Yl7qDmtNIvO6PgRtvOzrYHDdn5i47ff+OYU6ppM7mgiwbUwI6H0wI6O4liu3hLfJKa6+QvbCgAAaV1iMSG1PdK6xCJAq0Fxaa3H2kmUZfnuQgxckkPndXyU2H6B6yynmlftifuoNa00iyY6fsKVzs/2dd7d/5vs7SPuQSt0vkuocC/fZDcrvwTLKGzNr0ld3CLq4Wy/AFxLYkII0JJtzRdC4Gmi4yec6fy4snkdKirHZcrSpBq0Que7xE5iLa9ztOBB/IeUxS2iLs70C6wArQbje6l7UEvkpea00iw6o+MnpHZ+fGd5MlPU/6b3FVoNwDDcNRB8Ia6WOCZ2Emt5ndCCB/EflJzANznTL7CaTQy2HqNdPtKivsmEg7+VIf36OG83xSW0o+MnpHR+js7yrD5Q7IbWES5RYTpoALtaKOxjUwYlmv9t+3VA/XG1xDExtXJi9UEwVl0x78hSKCOxRe8J39I/MQaGiGDer1vWYLNFCyHEVm5hmbeb4DLa0fETfTtFI0YfhPKaBs6vszsAfTtFY/DLux2e5SHupwGw+M4eAMCZHpjNkte7Y7TDrxPfJVTdnAFQVtOAmZ8cA9AyuLmvXwdPN5N4gZRq9xTe6lt2FRh5kwUJLYLRpJfYU//IjyY6foANQ3M0yQFaOr+Ve05THn0vCwnUYtl9qeaJimVBN9u6R7YF35yti0TUia1ubjvZ5WKsqsPS7FOIDA1E1ZUmD7WQeEKMXofymmtZGg2RIZg/JgkLd5yAsaqOwlv9BF9aaVZkmA6L7+xhtQhmWVev9FK9ZxpKVCOts7rD1gCa6Pg8oY4PuLYDAABLs095pmGE163dWvMWdOMi9HXi2ywnu8bqOizc/rPVoJfF9gH1lBre50zs1xEDOsfi+6IyAC39wYDOsdBqNZw7fmLCW+UoLE08R0yikVBdgNXhcq6zuFqN49IFxH9EhekwwAfGFjTR8WFiOr4YvQ57/zkUAVoNBi7J8VjbCL+sny8gK7+EQs+IaOxkN7ewjHOSY6mukSY6vmbFnkKs3FtoHqAu333aXAiaa8dPKLxVjsLSxLPEnK+xTD7BtwhKkxzCWnxnD59Y3KCJjg8T0/GV1zTi8JkKAKBDiArywrYCjEg2+EQnQzyHYuz9l+0Ala2Vs/KBPtg/O0P07gzfANjy9WiyozxSMqtSmnniSGRIIJbc3dNn7nPKuubDpHR8NEBSFr6CboQ4QgfLCcuyVg4ApHWJxYTU9kjrEuswXE2OwtLE86RkVqXsasSRNx/o6zOTHIAmOj5NSsdHAyTlocknkUoo5TTxL1yFoB1xprA0UQYx6ebZtNL02UL4xEeGYEBn9Z/LsUQTHR8mpeOjAZLy0OSTSMWmnKb1dmJJasFoua4jnsPe+wB3bTUGwH39OmD78fOUXY3w8sX6ezTR8WFCHR9w7U1NAyRliQrTUdpX4rRWwXT8klwjZXdfzuuIZ41KiceKSb0RrddZPa4PDoA+KABLs09h+sd5WLjjBByNZTUANL411iUC9EEBWOWj5+9oouPj2DobhkjrDyZDZAgdKlUw+owhUjWbGLyWfQqPrzuCy/VUJ4dY79qLISUKgChPVn4JFu44YZV5UQPgcn0zahqara7lO2bF/u1vvaG1expJFMn2/eFLaNnPD9jW2Si/XI8YfRAiQ4PQbGIQoNWYD6ESZaiobTSnASWED1vrJLvAiM+PnkNFrePU0sR/iKmVY4vd2Xe29g7xHr5seUJRGrZ1c9jU4+cqrmD3r3/K3UyiUBr4brZXyTs6+/btw7hx49CuXTtoNBp88cUXDq/fs2cPNBqN3X+//PKLs20mTgjQalB1pQEvZ/2ChTtOYOYnxzDxnYMYuCQHWfkllIVFgXw1Dp76EHlk5Zdg4JIcTHznIFYfKKZJDrHi7K69WqIAqB+5xpV00SYGmD8mCa/dl4oNUwZg/+wMjEqJR4w+SPZ2EuUSSjTSbGKQW1iGLXnnkFtYpqrMi5J3dGpqatCrVy88+uijuOuuu0Q/79dff0VERIT5361b07aoJwnVRngsPcEbzSIO+GocPPUhruO7nwkBgGlDu2DmiG5Or8xaRgGIqb3jDdSPXOPqQuX5yiuYPKiz1WOGyFBXm0VUKLvAaBdJovYCwpInOqNHj8bo0aMlf6M2bdogKipK8vOI68TURvj4h9892SQiIEbvu8kIqA9xDRX7I0LSr2/t8qQkQKtRdOgs9SPXuLr7vznvHOaOsQ5JZM9rOZpAaTQAQx2RT7F9L/hCAWGPJSPo3bs34uPjMWzYMOzevdvhtfX19aiurrb6jzhPzGqPLx9EU6M7UtsravVUCagPaUFhpsQRZxdJ1ByaIoUv9iOu7v6X1zSaQ5bY98H24+dxX7+ODhPj0CTH99i+F3yhgLDbkxHEx8fj7bffRt++fVFfX48PP/wQw4YNw549ezB48GDO5yxatAgvvPCCu5vmN3YVGL3dBCLR8GSDt5ugGNSHWPPVs1tEHs4skqg9NEUMX+5H+ifGwBARAmO1833DxUt1nO+D4MCW9fD6JpP5MdsEBkQdAjRAs4i/G/sZI6WAsJJ3f90+0enWrRu6detm/ndaWhp+//13vPrqq7ydy5w5czBr1izzv6urq9GhQwd3N9UnZeWXYM2BYm83g4ikQcuhX18NW3MG9SHWfPXsFpGH1EUSXwhNEcOX+5FdBUbUNbkWlVFcWotl2Sft3geWE5ywoABkphjw2ZFzLn0v4h3to0JwtkJ4MhwT1pKIwlcKCHuljs6AAQNw6tQp3q8HBwcjIiLC6j8iHaWMVp+W6tUdvd0MxfPnPkSo1gnxX1Jr3PhKaIqzfKEfYSeqlTxZF4X6CQ0AQ0QwNhw6K3jur7ahmSY5KiZmkgMAD689hEU7C3ymgLBXJjpHjx5FfLz6V4iUjmL51Wlp9klz2m/CzZ/7ELbWCUCFZYm18b3iJYWtSQlN8UVq70fEJCZpGxGM6cOu5/wa+06Z2L+jS2FvxLeYGOCtfUXI+eWCTxQQlhy6dvnyZZw+fdr876KiIuTl5SEmJgYdO3bEnDlzcO7cOXzwwQcAgGXLliEhIQHdu3dHQ0MD1q1bh02bNmHTpk3y/RSEk9K3Ewk/XwsbsUR9iOvYWie28fStggNhYhjUWiQX0QcFULIRP7H1WAn+NSpJ9GRHzaEp1I+IW8w0Vtejf0IsZg7XYO2BYlReubbzwxYHtQxPI4S1en8RHk1PwOr9xbzXqKGAsOSJzo8//oihQ4ea/83Grz788MN47733UFJSgrNnz5q/3tDQgGeeeQbnzp1DaGgounfvjh07diAzM1OG5hNHlL6dSPgx8N1Kxf7ehzSbGFnqk/DVOgFgfiy7wIhtxykZib9gd1/6dorGh7nFOFNei04xYXgwLQFBgfYBHGoOTfH3fgQQPwGduv6I1QQnKlSHR9MTMS3jegRoNcgtLHNXE4mKmRg4nOT8bXCiKhZiNQyj/ASB1dXViIyMRFVVlSJjZJWq2cRg4JIcGKvqqOaGim2YMkBRGU3UeD8qpc3uyG7FN3Haefw8nlx/VK6mE5XIuLE19vz6p1VWLK0GmDIoEXMyk62uFfqMYJOj7J+dIetii1LuR6mU1u7cwjJMfOeg5Oexf0k2YoB9H1CoO5Ei3g19gxRi70evnNEhnuEolp/9d1SYzqNtItIpMWyESMceGrYdTLBhis6cycrKL8HAJTmY+M5BTP84DxPfOYiBS3Kw8/h5PLclX66mExXJ+eVPu9S/bMz9op3WyWnEfEaoITTFXzmbmMQ20YTl+4AQsdRyfo8mOj6OjeU3RFqHHhgiQ7DqgT44/NwIfDT5ZoQFBXiphUSIEsNGiDTuyG7FN3EqqarDk+uPoryGOwsT8V/vfFuEBpvzGI4+I6ScEfSXgqNK4kpiEttEE6NS4rHqgT5oFUxjASLegdN/Kv5ed3sdHeJ9lrH8xqorKK9pQHRYEM5VXMHWvHNoEx6CoAAtakEHlpVGDRlNiDC5C681NJkwd3M+haQSSUwM8GFuMSYP6mz1ON95L7E7Of5QcFSp+BKTRIXqrM7l8LGMGBiVEo8rjSbM3JjnjqYSH7R8dyE2HTmn6HudJjp+IkCrQdWVBrz81a8Uh6si9/XrSGEjPkDO7FZZ+SWYu/kn2rEhTjlTXsv5eIBW49RZQH8pOKpkXBNVE8Pg/ne/F3xu6aV6bLm64Nk/MQaGCIogINIo/V6niY6f4PswIsqWEBfm7SYQGciV3YruY+KqTjHy9SlCIZm+mjlSiQK0GvRPjDFPduL0wTBEhDisj6PVAAt3nDD/Oz4yBPPHJCE+MoQWRIloSr/XaaLjB8QUFSPKROdzfAN7aFgou5WjMEW6j4mrtBrgwbQE2V5P7pBMXyBX+nipuMIHhc7e2h6tMFbVYer6o/jb4ES8ta/IHc0kPkrJ9zpNdPyAmKJiRHnofI7vYA8NP7HuCDSA1WRFbHYruo+Jq6YMSuSsp+MsNRccdQdvnVXi2+mtlVgomH3+xh/+wOCusdh3iurrEGmUeK9T1jU/cL7yirebQJxAaV19i6vZrZT4AUKU56ZOUbDtNrQa4O+D7evouErNBUfl5o708WK4Y6e38kojTXKIU5R4r9OOjsoJbZMv2lmAt7+lLWg10WqA5RN7K/JQH3GNK9mtlPgBQpTn8JlKvD6xNy5W1+FMeS06xYThwbQEWXdyWHKEZPoCb55Vop1eogRKvtdpoqNiQtvki3YWUJytwrQKDsTE/h3w7tXJJ9cH4/KJfZDZkyY5vsrZ7FZCg0pCWC/tPOGRiuVyhGT6Am+eVaKdXuJtSr/XKXRNpYS2ybfnncc7tJOjOAM6R2PemGTOEKb4q0VcaZLjH6QWWHSlOCDxH7aFIN1NroKjaubNs0q000u8Ten3Ou3oqJCYbfK5X/xkl1GFeF/2iT/x4o4CzBuT7FKBPqJuzh5a5isOSIgtY5Xnzma6WnBU7bx5Vol2eoknaQC0jQjG/+5JRenlelXc67Sjo0Jitsmr65o81yAiybv7i9DQZDKHME1IbY+0LrGK7iiIfFw5tNxsYhAZGoR/jboRd6S2c3dTiYot3HHCbQfgufhzf8ZONvh+Yg3cl0XT0U4v+++oMJ3V4zF6HQiRin0/LRjfHenXx2Fsz5bPoO3Hz4uKSvAW2tFRIYrJVTeGAf712THc26+j4ldCiLxcObTMtQtECJ+KmgZFVyv3Jd4+q8S302u4uktsu9tmrK7DzI15bmkL8V0Gi6gDb6VSdwZNdFRI7Pa3RtMyqCbK80XeeXyRd16xHQNxD2cPLfPVySCEj9KrlfsaocmGu/t4R+GDtivtbcKD3doW4hviI0Pw6t29UFpjHaLG93nERiUobXGFJjoqJDal59ie8ZSQQOFKFNoxEPdw5tCyO+pkEP+g5GrlvsjbZ5W4MjpyrbwbIoIRFaZDVW0j9SuEkwYtu5DpXeOsHvdmKnVn0RkdFRITk/v8uGTMG5OMvw9OtCseR5SFQUvHoNT4ViIfqYeWm00M3jtQROFqxCUU7uw5SjqrxHce8EJ1PSqvTnJoeEBsxTvIoiYlKkEpaEdHpcRuk8/JTMY/brsRH+YW40x5LdpFhGDxV796q9mEB626+ja2sK+x6gpi9EGoqGkQLLBIZ3KIIxoA0XodymsaBa+lFMT+pdnE4GBhGZ7d9JPDlffIMB20Gg3Kaxo83ELiTfGRIZg/JgnR+mBcvFSHuFbBAAO7EDUuxmrvpVJ3Fk10FIIdCEnZ7ubbJgeA3MIyq8cmD+oMAHgt+5TbfxbiHCV1DEQ+YicslruxuwqMdCaH8GLfK/+dkIK5X+SjspZ7sqPkauXEPbLyS7Bg688wVtc7vI4BeN83xHdNH3Y9nh52g1M7jVn5JVi4/WdR1yppcYUmOgrgSvYK25hcR681ItmAtQfozI5SKaljIPKQkkTAMkPSwCU5NMkhvNpGBGPB+O4AHA9WGSi3WjmRX1Z+CR5fd8TbzSAK9kbOaXRrGyG5MLnYzzIlLq7QRMfL5MxeIfRaM4Z3ReUVWsFRqgoKH/AZzSYGB3/jDx1hxeh1mD+2OwwR13ZxcwvLKFyNCNDAZGIw94t8h1dFhekwItngoTYRb2o2MXj285+83QyicCYGeHL9EazSih9bik2I44lU6s6gZAReJJS9AhB/SF3otRgAb+/7zfnGErdbuIMSEviCrPwSDFySg/vf/V5wYaG8phGGiBCrQ8sUwkiEXKiuw5PrjwqGHlXWNirqUDBxn4OFZRSKRkRbsPVn0eMNoQQErBh9kCIzyNJEx4vkzF4h5o1Y09AstYnEg5SWqYRIx5flyBHbiQ2FMBIhUpZDaOLs25pNDHILy/B+brG3m0JUxFhdj+U5p0VdK7YPeW5MkuImOQCFrnmVMzU1XH0tomz0d1QvZ+vd2E5shOpkESIFTZx9F2VmJK5Ymn0S3QytBCcnYvsQQ2SoHM2SHe3oeJHUmhpyvBZRNvo7qpfY7X2WBi2JQrgObd7XrwNNcojLokJ1ijoUTOTjzO4xIba4jkewu4Rb8s4ht7AMfTtFIz4yhLfmkqPPMiWgHR0vElq5lZK9glaB1U2JmUqINFJ24/gObdIKLZHT8KQ2ijoUTOTh7O4xIbZsa/jxZe4d3yseb+8rggbWobNKTUBgiXZ0vChAq8Hz45IB2FcnlvrmsXwtok5K7iiIMCm7cQabytPNJgavZZ/C47RCS2SUfn2ct5tA3EDq7jEhjrCLdHy7hMaqOry9rwh/G5wIQ6T155ztZ5kS0Y6Ol41KicfKB/rYzaANIuvosJpNDCJDgzAqxYAv843uai5xg6gwHRbf2UPRHQURJmZXNSpMhxUT+2CARZY1sQX+CJFKqTHzxDV0lpPIqU14iGDmXg2ArcdKsPefQ3H4TIWk4vbeRhMdBRiVEo8RyQYcKip36s1D4S7qtmJiH6R3pZVXtWN3VZ9Yd4R3e3/xnT2s/tZSCooSIoWSY+aJa+gsJ5GDZci82CzAh89UWBWpVwOa6ChEgFbj1JuHBkrqxXYyA1TWaRB+UnZoKc6euIsGFArry+hMLnGV7fEIY7V8WYCVhiY6KkYDJfWjwYjvEbtDS3H2xB3iJYY9E/VxtHtMiBiWi29Z+SVYuP1nUc9T424iTXRUjAZK6mWICMaC8d1pMOKDmk2MqDDUXQV0lo4Iu6tPe9xoCMeLO38RvHb+mCQ8kp5Iiyd+gG/3OCpMh8raRi+2jCjR8KTWeHBAAiqvNFp9LomNClJzZlia6KiYGrcQCXB3n+uw5O6eNBjxQXypObnC1r7IO++NJhKV2XTkHAwRIYgK06GqttHhgMRYXUf9ih/h2z3eVWDEgq0FosORiO8b16s9hnRrY/WY2KggNaSQdoTSS6uYGrcQ/V10mI4mOT7KUWrOJ9YdQVZ+ifmxQ0XlKK9p8HQTiUpdqK5DpcAkBwDe+bYIi3YWeKRNRBnY870TUtsj7Wo2x1Ep8TjwbAZmDu/q7eYRhSi9VG9XGFRsVFCMPkjxKaQdoYmOirEHEmnIrA4aAIvu7EGTHB8klJoTsK5ATSutRAo2vWtUqHAQxjvfFqGhyeT2NhFlC9BqMH34DVj1QB/E6HXebg7xsoU7TmDgkhyrBTexUUHPjUlS7SQHoImOqlGRUOWyncvEq6CoFnGe2NSch4rKJR38JITFAKi80iR4nYkBPswtdnt7iDqMSonHwTnDER1Gkx1/ZxtdIDYqSO31uOiMjsqNSonHikm9MW3DUZgo9YpisH+LyekJGJ5sUEVRLeI8sStj2QVGrDlQTFmSiFudKa/1dhOITMQmNxF6ziO3JGBp9ikPtZooEbsz/MK2Aoy4Oi5xlKZczQkILNFExwdE64NpkqNAGgA7842YO0adB/iIeGJXxjbnnaNJDnG7TjFh3m4CkYHY5CZinpOZYnB7e4nyWUYXpHWJFSxyrdYEBJYodM0HUPY1ZbLsUIhvEzovpwEQqw9CeQ2lfSXOiwoNtAuLtaXVAA+mJXikPcR9pCQ3EXpOSVUdVh8odmdzicqw40Y2Tbkh0nqxzuBD4fa0o+MDKPuastFE1Pc5KuDHjksnpLbDGhpsEBdUXmnCiOQ22FVwkfeaKYMSERRIa5hqJpTcxDL8iF1tF5MqWKsBRX8QANbjRrFFrtWKekMfUFFTL7jKR7yHJqL+QWhlbEQyhY4Q12gA5J+rxpRBiXZ9vlYD/H1wIuZkUoIatZOS3ARomeS8d6BIMFUwTXII0BLKaHvuhitNua+gHR0V4TpguKvAiKnrj1Lcv0JpNUDfTtHebgbxEEcrY80mxuHBT0KEsAPcjBvb4p8jb8SHucU4U16LTjFheDAtgXZyfITYKICLl+o4z+Q4Mqp7G2T9zL8jSHyfL5y7kYImOirB1ZkZIkJQ19RMgyYFMzHA4TMVSOsS6+2mEA9hV8a4HucLbyNEiouX6hAUqMXkQZ293RTiBmKjAIpLa7As+5SkvqRvp1jk/V4FY3W9c40jqqXVAMsn9vaJczdS0PKPBzWbGOQWlmFL3jnkFpbZVanls/N4CR7nOpR4tVo2UTY6o0NYfOFthEhB4bC+TUxyk/jIEGw4dFbygknllQZk9vCvgS5psXxiH2T2bOftZngc7eh4iDNpIgFg5/HzmLbhqCeaSNyEBiXEEhvetjznNJZmn/R2c4iK+EpdC+KYmOQm9/Xr6FT/sWJ3odVr0c6y74vVB+HFO1L8bieHRTs6HuBMmkj2eU+up0KgShURHIDwEP61AnbVjQYlhMvHP5z1dhOIivhSXQsiTCi5SUKc67WSaGjh+6LDdMidM8xvJzkA7ei4nTNpIi2fR5Srur4ZM4ffgGVXV9Vs/8YMgPkuFAt1piI2UQehrEqE2DKIiAAgvsVRcpPcwjJvN4+owCO3UJISmui4mZQ0kf0TY8wdWumlehoIqUBCXBhWPtCHN+vNwh0F0GoheXDibKgjUa4rDc14aWcBistqYaJtWiJCjF6H+WO7wxBBCx3+ii+5CXuOh7I4Ekcam01oNjF+3Xf49zTPA8QeRM8uMGLgkhxMfOcgpn+ch4U7Tri5ZUQOpy5cRmRoEOaNvpHz60LhiVycDXUk8nM2gYitKR/8gKR/Z+HDg2fx7alSHKDVWCLCS3f0wPheLYeHtx8/79J7kPgW9hwPAN6kBZPTEzBtaBfPNYoozvLdhRi4JMevxw20o+NmYg+ir6aK6aq0fPdpLN99mrdgq6PwRC7OhjoS+cm1qzblgx8cVrInhMvM4V0BAAOX5NDOLuHEnuNx1E/lFpZhuUUCAuJ/2EXSlQ/08ct+gyY6bia0vawBoNFQxWK1c/T3swxPFKqnIyXUkWrzuA+7q2b7Z5X6gXGloZkmOcQp1VcaZXkPEt/m6BwPAFTUNHi5hcTb/H2RlELX3MzR9jKb2pEmOf5BTBijlIrYxD2EdtWAlg8MoRCiZhODpzcckb19xD9szjvn8nuQ+Af2HM/YntZhjg1NJizcQUmNiPUiqb+hHR0P4NteNkSGYHSKAWsobM0viAljFBvqSLV53MfZBCKWK6lcYW+EiKEBEKMPQpmDlXj2PfjegSLEhQdTVkbC2efE6HUor6Gi4uQaf1wklbyjs2/fPowbNw7t2rWDRqPBF198IficvXv3om/fvggJCUHnzp2xatUqZ9qqaqNS4rF/dgY2TBmA1+5LxYYpA7B/dgZGJBtEPX9e5o2IDNW5uZWET4xeh8npCU49V0o9HbEVsdVcm0fpfYizCUQmvnMQA5fkYNHOAs5kEoQIYe/7Caniqpcv3HHC6r3nTweOld6PuJNtkpSdx89z9jk0ySG2/HGRVPJEp6amBr169cLy5ctFXV9UVITMzEwMGjQIR48exdy5c/H0009j06ZNkhurduz28oTU9kjrEosArUb0wLZ9VBiaTSZPNpdcpdEA/xmXguEiJ6VWz736v2KL/AmFOkp5LaVSeh8iJYEIV2a8t/YVUbpX4hS2GKTYBTBL/paVUen9iLtk5ZfYLbBM23CU+hwiKDpMp+pFUmdJDl0bPXo0Ro8eLfr6VatWoWPHjli2bBkAICkpCT/++CNeffVV3HXXXVK/vc9hB7ZPrDtiPrPDYoey43vFY+p6+0OpxDMYBnjq46NYMam3YN0CrU1iCWeK/DkKdfSFbEtK70NcSSBC9yhxxat390J61zg0mxjJNVL87cCx0vsRd+BLkkJHtYgYFbWN2FVgVP0YQiq3n9HJzc3FbbfdZvXYyJEjsXr1ajQ2NkKno3AsRwPb+WOSsHDHCRpAKcDCHScwf0wypq7nn5Qun9gH0fogzuw3Ughl0vEnnu5DhBYfGLRMfgmRW2lNPQDH70FHKCsjP7WPRRwlSSFEDH9aCLHk9omO0WhE27ZtrR5r27YtmpqaUFpaivh4+5llfX096uvrzf+urq52dzO9bkSyAeEhOuQWlgFgkNY5DgO6xAoejCaewQ4govVBnJPSGH0QFk5IQWZP+VZK+Cpi+xtv9CGUQIR4g2XYJN97UAx/PHAsRO1jERoLEFf560KIR7KuaTTWM0fm6nKo7eOsRYsW4YUXXnB7u5SCK1vKpiPn8Py4ZNQ30bkcJbl4qQ4TUtvDZGLw3JZ882HPspoGLNxRAK0Wfrct7Ane6EP4dtUOFZXTRIfIis20Zqy6gtzCMvPure17sPRSPRbuOCH4ev544FgMNY9FaPJK5OJv7yW319ExGAwwGo1Wj128eBGBgYGIjeWeUc6ZMwdVVVXm/37//Xd3N9Nr2JhbrkPNT6w7guLSWi+1jHApvVSPncfPY+r6o3YZbfztMLCneLMPcSaBCCFSMWhZLJn5yTG7DGqW78FH0hN9Piuju6h9LEKTVyIXf3svuX2ik5aWhl27dlk99vXXX+Omm27ijYkNDg5GRESE1X++SExhwo9/OAtDRDANqhRi4Y4TvBluqJCfeyitD3GUGY81ZVAC4iO5P0yiw3QOn0sI36KJP2RldBel9SNS0QILcZW/LoRInuhcvnwZeXl5yMvLA9CSsjEvLw9nz54F0LIC8tBDD5mvf/zxx3HmzBnMmjULJ06cwJo1a7B69Wo888wz8vwEKia2MOHE/h0B0MBIKRzNYfy5+rBYvtCHsOcnDDyTme3HjZg/JhkbpgzA0nt6Yf6YJCy9t6V+1o/PjcAqB88lxNGiCd97j01N7S+hs77Qj0hhOcklRCp2/Hhfv47Yfvw8cgvL/GZBVvIZnR9//BFDhw41/3vWrFkAgIcffhjvvfceSkpKzB0NACQmJmLnzp2YOXMmVqxYgXbt2uH1119XTTpHdxIbJ5kQp3f6UCrxDn+LgZXCV/qQUSnxMJmAJ9cfsfuasaoOU9cfwcoH+uCOPtdxPjfjxrYYsCibivoRTo4ODlNWRt/pR6QYlRKPFZN6Y9qGo5RSmkgSFaYDA2Bp9knzY/E+UrJCiIZhlJ8otbq6GpGRkaiqqvKpMLbcwjJMfOeg4HUbpgxAWpdYNJsYHCoqx5f5Jfgg94wHWkicxf7NfJEa70d3tLnZxGDgkhzexQcNWlbZ98/OsBuANpsYvHegSNTBcuLfXrsvFRNS23u7GbJSYx8CKKPdYscNhADArTfEoXfHaCzNPsV7zeT0BAxPNqhusUTs/ej2MzqEn1DMrW08JXsodbSPz77VLkavQ99O0d5uBnEzsaGntmGMbGVzmuQQMfzt4DBxjKIFiBRTBnfBxz84TqKx+kCxXRIUX0ITHS+SerC02cQgt7AMXxcYQZSrvKYRQ17Z7ZMdBrlG7IDD8jq+LIuE2PLXg8PEMZr4Eimk1F/y1cyxNNHxMjbmNlpvnfXF9mApuwo88Z2DWEs1PBTPVzsMco3YAQd7HVU2J2JRBjXCh40EcUWojoZ+/uL93GLR1/pq5lh6t3tZVn4JFu44YXUgOUYfhPljkq0mObQKrC6+2mGQa6SGnlJlc8IlKkyHqDDHC12EsOTIvpbZI57KVviJylppyW64Qq7ZaKIteedUma1NctY1Ih92AmP7limvaWjJ2KTtgxHJBloF9jCdFmg0uf46jrImEfVjBxxPrDsCDWB1j3KtyFNsPbF0e2o7/KVvBwy42jf4cwY1Is2olHi86UL2tU1HzkEfFEDjCj8RFapD1ZVGSX9v9vMqK7/ELuOv2rK10Y6OlwiFsTBo2Q04WFhGq8Ae9o8R3RCjD5Lt9WiA67uk1DSh2Hpi6Yu883jms2PYVWA0J5qZkNoeaV1iaZJDBGX2bIflE/s4/fyahmYZW0OU7OFbOgGQVouxTXgIbzSR2kLzaUfHS8SEsZRU1SH3t1IPtYiwquuaUF7TINvr0QDXt9nWNInTBwMaoPRyPXILy8yhayYTg6hQHSqvUN0ctbPdwXMWO2CgMDXijMye8Vilta+x1yo4EIFaoPJKkxdbR5Ri4w9/4G+DE7H1WInguJMti9C3UzSGvLKbs59jrl73wrYCjEg2KH5hhiY6XmKsuiLqOpWFQvqEN/cWyvI6bIdBWZN8H7sin5Vfgmc+O2b1YcKev5AaK20pRKdFnRzxlEQWr0/sjU9//B37Trm2EKW2AQNRHr7isQCoVhcBABir6/D2viKsmNQH0fog7CowYs2BYoch14fPVIgun6D00Hya6HiJ2B2D6LAgWgVWIcqa5H/4zty5MsFh0SRHWeZvyZfl7wqoa8BAlIldaLEVFx7shdYQpVq4owD7Z2cgrUss+ifG2O0EGizO3mzJOyfqNdUQmk8THS+JaSWuA4oLD8bArnHYflwdsZBqcv/NHaDRaLAl7zwu1cm7xW9Q2WE94hpnU0cHBWjQ0Ezbtmoj1yTHkhoGDERdKGyasGwXVPh2AtmFWanlE5SMJjpeYogQ+yYKxn4XwyMIt8we7aDVaLDu4FnZXjMqVIcV9/fBgM50oNifOJs6miY5hKWGAQNRl/6JMRQRQqxYLqjw7QQC18onGKvqOBfw1BSaT1nXvERM0a/4yBCYmhnqpNyk9HK97KuolVcaodVoaJLjZ2g1njjLtt4SIXJoNjEUDknsiF1QsazXZDuaUVtoPk10vIR9E2nA/SbSoOVN9H1xmecb5yeKS2vcsopKg17/Q6vxxFkM1DNgIN4npnhjVn4JBi7JwcR3DuLLfKMXWkmUxpkFFSnlE5SMQte8iH0TOToQln+u2ost9G1Ls0/hzUmtHG7POoMGvf5HaJuf+Jfo0EBUiEztGxWmw4hkg5tbRHyBmOKNfElRiP9yZQdG6CyPGtBEx8uE3kRpXWKxfPdpL7fSd839Ih8v3d4DU9cfcfm11BSzSuTF7tA+se6IbDVWiHrVSzh7VVnbSCFGRBDfBMayFtOIZINTSVGI78hMaYuDReUor7l25MHV5EiOzvKoAU10FIDvTdRsYgAGCNVpcYXSy7pFZW0jTl28jJUP9MHczfmi0347yj+vppUOIh++HVrif2olVp3nCndlz1iodRWVyMdRVkf2sRe2FSA8WEd9j58bmRKPNyb1pb7DAk10FIpri5q4x9rvinD4uRHIuLEtBizKtloJscTu2Mwfk4SFO07whhsS/2W7Q3vqwiUs3y1PAVriu2zDXcWEKBH/ISarY0lVHXJ/owytvqZvxygcPlsp+vo24SGq34GRG010FKbZxGB5zikszT7l7ab4jcraRhz8rQzp18fhpTt64Il1LWFsfDs2o1LiMTIlnlZMCCfLD5ncwjKa6BCHwkMCrMJdxYQo0WTHv4hNcHOoqNzNLSGeJmVcQdkbuVHWNQXJyi9B+uIcmuR4wdSPjiArv0R0lhF2MDshtT3SulDNHMKNTVJA7w7CZ9HtPcz9h9gQJa5MW8R3iU1wc6i4ws0tIZ4m5W9KofPcaEdHIShTindVXmnE4+uO4M1JfZDZU/1ZRogyUJIC3xMSqEFdkzx/yeFJbTA2tb3530IhSrbVzYl/6J8Ygxi9jjesmvg2oc8OjQZYMZF2evnQjo4COFrFI541bcMRbM87R5McIhu+XcLoMJ2XWkRcIdckBwB+Pl+NrPwS87/FhihRrS7/EqDV4A6LCbE7BAfScFCphHqcFRN7Y2SKQbC+kr+iHR0FEHPQkHiGiQGmfZxn9RgdAiau4kojv//Un1ixh87vqEWYTotambNf2p67ERuiRLW6/M/wZANWHyh22+vXN1FmVyV7LD0BO38qgbG63vyYISIYC8Z3BwAMXJJDyUt40ERHAXYVUOViT9AHBaBGYtpXgA4BE3nYZsI5cJoyJKmJ3JMcoGWlVoOWczcjkg2ChWepVpf/8kRRYq2mZbGPKM+wG9ti3phku2iTXQVGSl4igPYqvazZxOCLvPPebobPi9UHYdX9fZ16Lh0CJu5AZywIYH3uhj3TBcAugQXV6vJvjt4bcqGPNwXT2CdBajYxmLs5n5KXCKCJjpcdKioXXaSSOG9Cajvc0jUO8ZHOhXxYDkYIkUNVbQM0NF71OfGRIfj74ETJfQ177kZs5kfif/jeG8T3XbxUb3UGZ+fxkqt1//jHjzRuaUGha15Gh0o9Y0Sywbwi9vjVOjnOoL8XkUNWfgmmrj9KCUh8SFSoDivu74MBnVvSzf9rVBIOFZXjwOlSLN99WvD5luduuM50UVIUAti/N4pLa6gkhYqF6LSoExEWu3D7z05n3fP3cQtNdLysuLTG203wC58dPosv80vQKSYMr9+Tihmf5jm1TU+HgImrKMui79EAWHxXD6RfH2d+jA0z6Z8Yg01H/pB87oaqmxM+tu+NboZwLNhaAGO1fw9o1aiu0YRWwYGoqW9y+JngSmpxfx+30ETHi7LyS2glxkM2Hbl2DkqrATJubIPsExdFP58OARO5UJZF3xIVqsPiu3rwhpQ5qqXkyrmbZhNDOz4EwLVdnje+OYVl39CYQm1MjHuWvWjc0oImOl7CruoSzzMxQPaJixiR3Ab556oFB510CJiI4Wjgafm1L38qEXgloiYr7u9jtZPDhT1b8cK2Aqv+xuBkCtis/BK716J0sv4tQKvBjBE34EpjE97aV+Tt5hAJahuaMbZnPLYfl/+zgcYtNNHxGlrV9b5vTlzEzy+MQt7vlebBaUVNAxbukGcwQvyHo4EnALuvEd8QHxmCAZ3FhZfJde4mK7+E0skSXnMyk9Hruig8tyXfpXAn4ln7T8lbbiBWH4QX70ihvgA00fEafz8cpgQmBlj//RlMHtTZ6vGRKXQImIjnaODpSuILonxSV0tdPXfj6HyXbU0e6rP8V2bPdhiZEo9DReU4X1GLZzYdh5uio4hMKq/INymN0euQO2cYggIpsTJAEx2v8ffDYUpxprzW7jE6BEz42Ian9e0U7XDgSXxTVJgOi+/kP5cjltRzNkKRAJbpZKkP82/s51huIWiSo3BhQVrUNrhekJjtOV66owdNcizQRMdLPFHlmAirrW/ydhOISnCFp8Xog6gOlh9pFRyIvw5MxFPDurq8Y+LMORuxkQAUMUBY9F5QvtEp8dh05JzLr0Nh9txoouMljjLxEM/59tSfaDYxFOZBHOILT6NJju+z7J8v1zdh44+/48b4cJcGE86esxEbCUARA4RF7wXlG9i1Nb4rLJO88D1j2PW4uXMchdkLoL0tL6Iqx9534VKD31cNJo5R3Rv/xjcZycp3LkOS0DkboOWcTTNHoS82EoBvKKNBy66Qv6eTJdew7xmiXIaIa4lrpExTElu3QlqXWExIbY+0LrE0yeFBEx0vG5USj/2zM/DR5JsRFarzdnP8Em3tE0coQ6LvCguS/hEoNBkRIuWcjS02EgCwHxBRGnzCJUCrwfwxSd5uBuFguTDhzMI37daJQxMdBQjQapDeNQ6L7+rh7ab4pTbhIWg2McgtLMOWvHPILSxzagBDfBNNhH2P5up/fx98vVPPdzQZEeLqORu+AZEhMoRSS/s4Zz+novXBbm4ZcZblwoTYhW/auZWGzugoyKiUeDyWnoA1B4q93RS/wFYNrqhpwMAlOQ4PBVMVcv8ldtUsRq/jrFsRHKhFfZPrGXWIfCKvZk0bkWzA2u+KUFnrXGpXoUkLV78hxzkbuWryEPVwpUgsLdYo098GJ3L+7bRaDe7q0x6rOcaCtHMrHU10FGZEsoEmOh7Adg/je8Vj6nrHh4IB+4KPVIXcfwhlSGQnzPNG34hpH+fZfZ0mOcoTqgvAiGSDy6/jaDLCNzCdPyZJ1PtJaLWW0uD7h2YTg+U5p7A0+5Td18QWiaUQJ+XRANh6rAT/GpVknrBw9RlaTUvNPxZlVpOOQtcURuiwKZGHITIEKyb1xtZjJQ4PBc/5/Cc8vu6IXUy9qweSiXqIORcxf0wyXvzyF4+2iziPDTs7VFTu9G6OISKYdzLCZlXj6jemrj+K8b1aBil0zoY4kpVfgvTFOZyTHED8eTFKSKA8tuGvfH0GWwPpsfQEbJgyAPtnZ9AkRyKa6CiMo0EVcd20oV3MnUW0PljwUHAFzyDI1QPJRF2EzkVE64MoYYHKXLxU51JIT12TCbsKjHaPi8mqtvVYCVZMonM2hB878DVWO36Pijkvxo4raEyhPBcv1YnqMz4/cg4mhuE8p0VnjB2j0DUPE3PWgx1U2W5hEtd1bRtuDvdwNW6ZqpD7F0fnIrbkiSv2FhmqQ9UV53YQiLxcDeepqm3kDBsSm1UtWh+E/bMz6JwNseNMSnuhz7NRKfFYMakPpm04AhoHK0eb8BBRmT0rrzTi/ne/twtliwprSVpguTNNofXWaKLjQVIOE9oOqk5duITluws93WSfYzm4kStumQ56+i6uhQmuSa3Y99IjtyTgtW+4w1CI51hmLHJ0Xgawj5FnWe7qjkg2mCcoUrKq0TkbwsWZlPbFpbWC10Trg2iSoyBRoYHonxiD7cfPi36O7d+PK/RW7Nktf0Ghax7iKGab76wH+yE4IbU90q9v7ammqlJQgBYaBwuhXOkY5ToPRQc9fVNWfgkGLsnBxHcOYvrHeZj4zkEMXJLDea/27RSNGL3jdKCGiGBs/OF3N7aYiMWegWFrjPAlBQC4JzmWbMOG5MiqRvybM4tny7JPCp4ZpUU5ZWHz1MjdF1BovTWa6HiAK5WwWXSY0LGGZpP50J4tvgO+QofMNWjZFqYq5P5HysJEVn4JhryymzO1NHDtvTWxf0fBeHvCr1tbvcuvodUAb066tsqZlV+ChTtOcF5riAzB5PQEUa9reVZHaAGF+g0ixNmBr9A4wpnXDQqgUEp3uVzfhENF5W5JQuVKrS9fQxMdD3ClEjbLclBOpHF0wFfokPniO1uKuFJ2JP8hZWGCb0JkiX0v0dkc1yTFR7r8Gq/f2xuZPa9Nchz97eaPScZwkSmot+SdNw8wxWTpo36DOOLMwFfMOMKZBdOGZtoRcCc2hNVdSahoF4/O6HiEs5Wwm00MDhaWIfe3UgAtYWxPD+2C1+msjkMaADH6IDw3JgmGyFCrA75cZy6Eiu9xJYagXPa+S+zCxHenSzF3c77DA8Ox+iDs/edQBGg1mLs5X/a2+guNBvgiT3wcO5e/D07E2NR2AMQd9p77xU94497eiA7T8WZfZJXVNFglJeFLKEP9BhGDHfg+se4INICkpATGqivILSzj/CxjX/fxdUfc0m4iHbvL5q4kVBQiSxMdj3AmZjsrvwTPfv6T1UGz5btPIzI0EPqgANQ0NMveTl/BoGXgYYgMtTroK5QMgu9QMFUh9y9iFyYe/+gwauod34dlNQ04fKYCAFBe0+By2/wVX1iqLY3G/lp9cABeuasnMnu2Mz8mKstRbSMeXHsIYUEBor637fuG+g3iCmcHvgt3nLDqa2wTHo1Kicfk9ASspsLkXmcbwsr2GQcLyzB1/RFUuhAFILbwsD+g0DUPkBKz3Wxi8Fr2KTy+7ghnNo2qK000yRHJcuDhTDIIS5aJIdK6xNJgxYeJXZgQmuSwXK3XQsTjmhDV1jdDa3O/Svl71Irsb7myXlG/QVwxKiUe+2dnYMOUAVh6b6rDhCcs2wUVrs+4jKS2sreVSDc6pWUhxPJcVYBWg/SucVh8Vw/zWWGpKETWGk10PEBszPauAiPSF3+DpdknPdo+X8UOWOVIBkH8h9wHQ9uEh6C4tEamVyNchD7Lbe9vd4RziMl6RYhU7GT5jt7t8dId0ge/nJ9x9FGnCGsOFPNm8+Q7P2zb10WH6cy1dFhUeNgaha55iFDMNgA8se4I9T8ysN2ylZIMgmpaEFfi4y2x78O+naIxc+NRGVtIbDlao+C6v9nJrKP6Oc6wralDiJz4xhExeh1v1kfA/h4oran3QGuJWHx1b7jCX/t2isbhMxVW4bAAKETWAZroeBBfzDYADFySQ5McGXBt2TqbDIL4L74BRVSYjjOklM/z45Jx+EwFjNXODyzG9ozH9uO0U+Aqy/tbrsmsJVowIZ7ANY4wVtdh5sY8weey9wAdUFcWBi1jF66FEq6iwlz9C/U5/Gii42Fcb9rcwjJZs2z4izE94q8OIh1nNaICfsQZXAMKk4nB/au/F3xurD4IL96RglEp8diSd86p7x8VpsPiO3tgRLIBPxaXuzRZIkBcq2CrbFQjkg1uyXJECybE3WzHEbmFZaKex37GidnR1Gk1aKRwbo+hhRL3oYmOAtAHo3RvTuqNzJ7tONNF227ZCnXqlJ2E8LEdUDSbGMEBQoxeh9w5wxAU2HIEUuwEel5m0tVaOwzSOsdhgMXh9QXju+OJqylhaeghXavgQMzamIcLl65NFtlsVPtnZ+Dgb2WY+hF/liMNgGiB8CAWLZgQT5P6GSdmR5MmOc57c1JvaLUaLNhaILlINI0H5edUMoI333wTiYmJCAkJQd++ffHtt9/yXrtnzx5oNBq7/3755RenG+1r6INRPLayOZsqVkxWIyrgpzxq7UOE3ksaAC/d0cM8yQHEZ118bGAinhnZDc+MvBHpXeOs3o9sKF3biGA5fxy/cbm+yWqSA1yLi99VYET69fxZjth//3dCiujsmcQz1NqPyE3sZxzQsvuz+eg5nKu4godv6YRofZDnGurj4iNDsOqBlvHJqJR4HHi2JWPea/elYv6YJFGvQeNB+Ume6GzcuBEzZszAvHnzcPToUQwaNAijR4/G2bNnHT7v119/RUlJifm/rl27Ot1oX+NMtWJ/ZWLgVMfMl8GEspN4ntr7EKnvJbkm2qNS4nFfv46uNJ1YsM1GJfR3zezZjhZMFETt/YjchN6/QMtZ4InvHMTMjXlYuOME3vvuDMprGhCj1+HRWzohhiY9TgkLCsBHk2/G/tkZVv2/5ULsI+mJtFDiJRqGEVuKrcXNN9+MPn36YOXKlebHkpKScPvtt2PRokV21+/ZswdDhw5FRUUFoqKinGpkdXU1IiMjUVVVhYiICKdeQ+my8kuoWrFIr92Xigmp7Z16rphQN+KYq/ejr/QhUt9LQgVrhVAf4ZgGQNuIYNQ3mVAhIWEEAGyYMsAcoij0d3X170jkuR99pR+RG9f7d1eBkbK6upllHwJw/x2+yi/Bk+vtM3CyvQstukoj9n6UdEanoaEBhw8fxrPPPmv1+G233YbvvvvO4XN79+6Nuro6JCcn47nnnsPQoUOlfGufNyolHm9O6o2p649SZyTAla1drmQQxHN8qQ+R+l7iy7ooZqLN1oIijk3s38mpOmS2Gdkc/V1d+TsSefhSPyI3rnOFfHXkiHxsC5RzZezkw5VEichH0kSntLQUzc3NaNvWuqpu27ZtYTQaOZ8THx+Pt99+G3379kV9fT0+/PBDDBs2DHv27MHgwYM5n1NfX4/6+mvx1NXV1VKaqVqZPdth+oXLWPbNKW83RbFoa1fd/L0PcXaiLVQLyt+xOyr1TSanni918YQWTLzL3/sRKajv4PbAzR0RERoIQIOPf/gdFTUNLk0G2T4kK7+Ec/fMUVmC+WNokuNOTmVd02isV64YhrF7jNWtWzd069bN/O+0tDT8/vvvePXVV3k7l0WLFuGFF15wpmmq99Swrnj7299Q29Ds7aYoEsXA+wbqQ6ShTDzcwoIC8PfBnTEtoysCtBrRaXZZUjMuUuirslA/4lizicGB0396uxmKtO77s+YFkp7XRZqzWkpl2Yc4s3umAbBwRwFGplChYXeRlIwgLi4OAQEBdismFy9etFtZcWTAgAE4dYp/12LOnDmoqqoy//f7779LaaZiNZsY5BaWYUveOeQWlqGZI31jgFaDvw/u4oXWKd/k9ATOVQ8xv1eiDNSHOIcy8XCrbWjG0uxT2FXQ8n7q2ykaMXr+EBFLfAkE+PqTrPwS82Hu6R/nYeI7BzFwSQ6y8qmYq6dRPyKMfb8u310o22t2idPjySFdEB3mG5VJSq5mXgRazsdEhYrrO1i2fchBJ2oiWtbPIe4h6d0aFBSEvn37YteuXbjjjjvMj+/atQsTJkwQ/TpHjx5FfDz/Nl1wcDCCg30rjaqUA6zTMq7H2u+KJFVg9wfDkw12j9HBYHWhPsQ5Ygr8WQoK0KCh2X8m/Au2/gyTqWVlVEytG4A7Lp6vPxnfKx5v7yuy+92zKarpELFnUT/iGF/4lKsKS2vw5l75Jk5KwACY8/lP+PG5EQgP1okqCM2y7EOy8kvw7KafnG4H7dq7j+Rp+axZs/Dggw/ipptuQlpaGt5++22cPXsWjz/+OICWFZBz587hgw8+AAAsW7YMCQkJ6N69OxoaGrBu3Tps2rQJmzZtkvcnUTC+TofvQzJAq8HiO3tQhqWr+MJLpP5eiTJQH+IYX3jU8+OSBfuEx9ITMOzGtnjq46Mor2nwUIu9z1hdjyfXO/7dGCKCMbF/RyTE6XmzqXH1JyVVdXhrXxHnazJo6Z9e2FaAEckUeuJJ1I9wo+QD0lXUNmLa+iN4YEAnGCKCcaG63mFB6Plju8MQca0PkWNiSbv27iN5onPvvfeirKwM//nPf1BSUoKUlBTs3LkTnTp1AgCUlJRY5bFvaGjAM888g3PnziE0NBTdu3fHjh07kJmZKd9PoWCOOh1HH5KjUuKx6oE+dquLSqEPCgADBrUNzh3+FctReIkzv1fifdSH8HO0Qzki2YCoMB3vTq8GwJf5Rgzt1kaVkxx9UADCQ3RWlcQd/bxSxOqDsO9fGVaFXC25Mji0DD2hBAWeQ/0IN2eSD1juWgLwy0nSl/lGfJlvvDq2sceOIl66o4fVAqqrE0up5wSJdJLr6HiDGnLX88ktLMPEdw4KXmebg53VbGLw3oEiLNxxwh3Nc9qqB/qgoaEZT39yTJbX02iA4UltkH+uWlQYmqu/V+I8Nd6Pamgz36og+wE7Y3hXLM0WzsioDwpAjUqTmXw0+WZotRrzbpbJxEgKJXHEUV8gtj9xxJX6Xv5GDfcjFzW0e0veOUz/OE/wujtS22HwDa1hiAy12plQ6uKqp2lgPeFzdSzC9z0Aqp/jLLfU0SHSiY275LsuQKtBXLiyYoSX39cbALBAxslXRLAWDw1IQOnl+pZKza2CrbaGbbn6eyWEi7eyaonZoVx7oFjUa6l1kgMApTX1VpOFZhMDQ0SI1S6Psxz1BXL0ExR6QpRA7PswpX0ktDZ9m2WNqC/zS/BB7hl3NFEV2L74sfQEjEg2uDwWCQsKQFCg1mqH2hAZgvljkhEZGoQteecok6Ob0ETHzcR2OnH6YOQWlnEOsJT2AVpYWoNl2Sdl3d6uqjPhwTWHzP9mV0/4bnixvxOl/e6IcnkzsYVQuAkDoPKK7ycnsb1fA7QaLBgvfDbJmdcW+zUhFHpClKR/Yozg4oBWA6soEct+zrJGlJInOlFhOlTVNro9zO7LfCPmjXF9LPLOQzdhQOdYq4W0ipp6LNxByZTcTVJ6aSIdmy2Jb36uQcsN+49Pj1mlLe334i4s3PYzcgvL0LdTtMPX8LR3vi10e+fCpn3kS93akkY2iPf5GlBxUSIeGzZmO9koqarD4w7eh3IRuyqoDwpwazu8SR8UABPD2KWHZ88rclUWjwrT4c1JvREfKTzYqKip5/2aUD/Nsv063xlCQrxlV4ERdU2Od3VtKzAYOT5vxYxd4iNDMC/zRgy5IQ6hOs8NJ8OCAjDo+liPnCWyTf1sm35eaHzG/p4GdI41TyInpLZH1ZUGTF1/1O4zh+tvQVxDOzpuxmZLemLdEbuYT/bfLVuZ1qu15TWNWH2gGKsPFFsdFLR9DW+4XO+Z0BgG3AkF2JV3vgPXNPggUog5TPrs5z+5NbGF2FVBNYelCalpaMb9737PuaLJhtQc/K3salFQBmmd4zCgS+zVv4lGMOvawh0nMPLqirUtoX4aAP42OBFbj5VYDUy4UlQT4i1C2b80GoDrVDZXAh8xY5crjc14cecvMv8UwmobmrHtuNFj4yF2IUoo/Txf30HJlLyLdnQ8YFRKPFY+0AcGm1XHthHBnKuUtoxVdXh7XxH+NjjR7jV8ne1qCt/KuyVDZAgd7iOiiclSVFnbiOU5p93WBrE7Cv7AckXTcvX0UFE5BnSOxTMju+GZkTcivWuceRAQ7WB3lyVUlI+vn2b7kzmZydg/OwMbpgzAa/elYsOUAdg/O4P6GaIIYhZsHKWe4ipcyXdPsOMWb9f689Sib5vwEN6xh6PxGd9YREyoMhURlQ/t6HiI5SE/c0YhhsH97wpnFGJn+FuPlWDvP4fi8JkKXLxUhzh9MP7x6TFcqBZXRFCt2NUUMR15jF6Hvf8cyptGlhBbYsPG1n5XhGkZ17tlhU1snRx/wPZ3z37+ExZsLbA6a8AXvy5XchKuftryvKTl+QVClMSZtNJcbO8R23sirlUwnvzosMvfRw3Y83d9O0VjyCu7He7A2I7PHCUWoGRKnkUTHQ+y/ZDckndO9HPZGf7hMxVWryHXQV05tQoORJhOg4uXLbKLRARjQmo7fHr4D9GVy1lsWI+Yjry8ptHud0SII2LDxiprG6lWiofwhfTyFQOWMzkJTWaIGsk1KOa6RyzvideyT6HqSpMs30vJLMPODp+pELUDI3bsQcmUPIuWvb3ImTexbWfGFhFUkrQuMdBqbQ9Na9C7YzQOzhnuMImArRi9zpxQgFZBiDv0T4xBVKi4e8hd761mE4NnP/9Jltfy5ZBudkX1hW0FVkkLxB6cpuQkxFe5OigWc480mxisPVDk0vdRC8uwM7nHHtRfeRZNdLzImbh8287sUFG51+Nkbe0quGiX2vJCdctKbM4vF/DSHSmiX+u/E1Ikp9mmVRAiRYBWg0fTE0Rd66731vKc06Lv46gwHVqO31tjH1s+sY/5HMnM4TfAEOFb9wNX/Dob+gdQZjTin8RmeOXrOwDhe+RQUbnPp7l/LD3B7vyd3GMP6q88iyY6XmT5ZhfCN8NXy+6F5UrsiGQDb7pYS38fnIjMnu3M/6ZVEOIu0zK6Onw/uvO9JXWVdPGdPRwems/sGW9OYTp9eFfs+9dQxOiVtesrB66zBI5+L5Q0gPgyMYNnob5D6B5x53gjKlSHNyf18WpfpUFL3RzbszXuGHtQf+U5dEbHy9g3u23KQkuOZvhq2r2wXIkdlRIPkwkOU8L27hht9W8xKWBpFYQ4I0CrweI7e3CmZnX3e0vKKunM4V3NH4CODs1bOnymQtS5uCdv7Yw1B4pR12iS9gM4QY60sFx9n1AyAUJ8Gd94wjYNOtc9AsCqaHnfTtF2B+vdOd54ND0BmT3jMTy5LQYs+oa3fIQ7WY5RLM/auGvsQf2VZ9BERwEs3+y7Coz4Iu+81U3OVauh2cTgUFE5jFVXEKMPQkVNg2yZ125o0woXqutQVeeeA4cXL9Wh2cRg4Y4C3mv48siL7cgJkcpb7y2xq6RRoTpMy+hq/rfYQ/NiX/9KQ7PbJzlhQQH4++AueOLWLjh8poK7v4sIRl2TibfqOZsJiW/1lJIJEH8mZvBse49w1YfRaqwLi8ZHhmD+mCTE6IPcMgnp2qYVACAoUIuX7kjBE1eTLDka10SH6VDhhtB9Y3Wd1aSvf2KM2z4fqL9yP5roKAT7Zk/rEot5Y5KtUjmCAUpr6pFbWIb+iTHYVWB0uAPkqpMXL7vldVltwkMk5ZG37QRoFYS4izfeW2JXSR9NT3CqHWJff8MPv0t+balqG5rRzdAKQYFau/7OWF2H8sv1iNEH4Wz5FSzLPkk7t4Q4Qcrgma/IqMnmAWNVHaauP4ourVu5ZaLzn+0FiAwLQunlerQJD8GKSX2wcIfNpCIiGBP7d0RCnB5twkNQUdOAuV/8JOp8o5Rd5IXbf7baBbdMa09jD/WhiY4CsZ1UVn4Jnvn0mNWNHhWmcyn5QHxkCOaMvBHztubjkpt2bPhYrsRuP35e1HP4VqNpFYS4i6ffW2z8t7GKvx5WdJj1bg4XdpfX9gNYzOsD8EjIGgA88+lx1NQ3o7K2ATH6IBgiQ1FRU4+Xs36x6+sA66KEtHNLyDVc9zwAzn6A71qh2nQs9prTf0pbCNUAiNEHoUxgcmSsrreqK8juIEXrgzknFVn5JZi63n6CxrIdKxmuvt7CHScE+0LbUF/btPY09lAXn5no8H3IqxXfKovQJCciJBATUtuhY4weNxrCUXq5HuU1DYhpFQxDRAguVtdh9ufHccVDgxqW7UosZVAjpIWj+G+g5d5ZdGcPh/0ZV+hJVKgOj6YnYFpGV0UVI71c34R/fHpM8Do2dG3m8K7mFVy19+uEyIXznudYHIiPDMH4XvHYeqzE6tr4yBDc16+D2yJDWAyA/onR+DL/gqTnlVTV4cn1R/HmpD6YkNre6mtiioeDAeZl3oi48BAYIq71HVqthvesDd/rsUVBucLpifJpGIaR62iH21RXVyMyMhJVVVWIiIiw+zrXDc9XQdvbxEzImk0MBi7JcbkDst3mXb3/N2SfuOjSazrL9u/B/ox8Kyvs7s/+2RnUqSiM0P2oRGpos7P9GN+iCCsqTId7b7oO674/i5r6Zplb7V7UD/gmNdyPXJTSbqF7Xgw5EoJ4glYDLJ/Y2yoDa25hGSa+c1DU87n6UK6+NlbErhMAbJgygHNHx9cW29VA7P2o+h0dvhuer4K2N4kdyAidXxHLWF2PpdmnXH4dPhoNIGaaHB2mw95/DkVQ4LVs5pRBjRBrzsR/i1nZrKxtxFv71Fnkz9FZPUL8kajdDBHUMMkBWs4KPbn+KN4EzJOdXQVG0c8v4RgLcvW1xqormPmJ8E4zVzi9mhbb/ZGq6+g4uuH5Kmh7Czshs53AsBOyrPwS82NqqY0zKrmNqOsqahtx+EyF/fMpjzwhVtjzQRNS2yOtS6zgRF+uRRGlU0ufSIi7KfmeN0QEC9bHc9a0DUex83gJmk0MPvnxD0nPZWA/FrTtaw2RoaJeyzacXsrYjniHqnd0XMnc5UlCEzLb2E+1nEvp0iYC+FlcKBzfQIWymBDiPCkrm2qmlj6REHdT6qR//pgk3GiIwP2rvxe+2AktOztHMK6nAZfrpSdSEhoLCiVt4UprL3VsR7xD1Ts6Ym94b3cMUiZkgHAVXiXQaoCbJVQBdjRQkbqKTQhp+ZD9Ik9c9kK1klJxvNnEILewDFvyziG3sEwRO/mEyIV9f5+6IH/5h7v7tBe+SEB8ZAhKa+plaI1j2487v7hjrLrC+zU2nB6A3diLL5xe6tiOeIeqd3TUkrlL6oRM6PyKEj6+TQyg1WhgiAiBsdrxz2eICBY1UCGEiHeoqNwr1cM9RcpZPYqRJ76M6/0tB3aX4qU7e2L/6VIYq52fqCzccQKv/qWXfI3j4cr4R6i/lFoUVC2L7f5O1Ts6QjsfUlYD3cmZCZmj8yuPpSfI2TynldbUY8H4ZMHrFozvTrs0hMhMCR+e0W6KxwfEn9WjGHniy/je366yXEgICtRiwfju0MB+N0Oskqo6gIHgmCxG774+Q0hMq2DBa0alxGP/7AxsmDIAr92Xig1TBmD/7AzOfkgti+3+TtU7OmrJ3OVM7CfAf37lUFE51hwo9kTTHSq9VI9H0hOx6oE+ePZz++rEUWE6LL6zB62oEuIG3v7wjA7T4btnh2HdwWK88vVJNDQ5X5vLEBGMf4/tjmh9kKSzehQjT3yZ1Axr0WE6MBBXR8d2l4JvN0OK0pp6wTHZfyekiCra6Q6GCHF9ptii0c6O7YhnqXqiA0jfavQGVyZkXDec2Ern7rZwxwm8u78Iz49LxuHnRuDgb2XILSwDwCCtcxwGXG13bmEZJRogRGbe7gcGXh+LtEXfoPKK4yLGQmYOvwHTMq53ql9QS0IaQpwhNsPatKFdkH59a/OAmiu5z79GJQkm/RmVEg+TqeXQvzOKS2sxfXhXzjFZtF6H/05IQWbPdrxFO4Vo0FLWwpnjd+6I7lHLYru/U/1EB1BH5i45J2RCldQ9ybZeUfr1ceavUdw8Ie4jpR9gvz5zeFckxOkR1yoY//gkDxeq6x2uRM7NTMLzW3+2im2PCtOhscmEbS4cCgZaVp8XubjjSzHyxJeJfd92bRtuNZHnmtSL2aVoNjFYuKNAWiMtLMs+iW6GVuYJ03Nb8s19R3lNIxbuOAGtVsM7HoqPDMHYnvF451v7ul/saG7KoES8fbUumJixj7snHGpYbPd3PjHRAcRvNXqTnBMyZ7eZ5Z4Y8YWHqKmQKyFqxdcPaG1WPbk+dBeM7y64EjkqJR6ZPeLNfVZxaS2WZZ90qQ+JCtXh0fQETMvo6vLAg2LkiS/z9Ptbjho9L2wrgMkETF0v/PnPNx7q2yna4cShd0f7r4sN0XMHNSy2+zMNw4ipbe9d1dXViIyMRFVVFSIiIrzdHEVpNjE4WFiGqeuP8IaQsKuz88ckYeGOE24rNrZhygCkdYlFs4nBwCU5vN+Hbc/+2RnUEaiQGu9HNbZZimYTY/Uh27dTNA6fqbD70LW9rqKm3q5P4Nt1FbqvHWkVHICFE1JgiAyVdQDAtkkoRp76GmVR6/3o6XZ7+v29Je8cpn+c5/LrxOiDeDOciWlzs4nhDIW3vN62L+Pr42jC4bvE3o8+s6PjrwK0GqR3jcPiu3rgiXUtcbVcq7PzxyQjWh+Ef43shvKaBvxReQWfHf4Dl+qkF97iw26zU9w8IZ7FtaNt+2++UFK2b7AcGAD2Z+tcWe2dMqgz7uhznVPPdYRi5Ikv8/T7W66dIUdpnIU+/7n6qU1HztktvvBF8aghuod4Fk10fISjONHxveKxcIfzmVRiwnQorxU+cMx2khQ3T4iyOAolnbq+JZRkQmp787VcE6LMFINT3zs6TIdpGV2dbbogipEnvsyT729PJjjh+vynkHfiDjTR8SFccaIVNfWYuv6o053WzOE34Ilbu2DIK7tFp1CkuHlClENKCuZdBUbegcZqJ1LaawAsurOH23dUKEae+DJPvb/FFCsPCwpAbUOzy9/L9vOfUsUTd6GJjo+x3LZl43udmeTE6oPw4h0p5tUTKdvnlFueEOUQG0p68LcyhwMNoCXJAcOIS2ji6QyLFLJCfJmn3t9CO0gAOMPkxeL7/KeQd+IuNNHxYa7E1D83JslqgCJl+5zi5glRDrEhormFZYL9BZvJjS97oz44APfd1AHDkw20o0KISgntIK18oA/mbs53eBaHi6PPfwp5J+5CEx0f5kqHYIgMtXtMyvY5xc0TogziQ0TFrc8+lp6AL/ONVvd1VJgOj96S6HThT0KIsjjaQRqVEo8rDc2Y+ckxSa/p6POfQt6Ju9BEx4c50yEIhZVJ2T6nuHlCvE9sKGla5zgs310o+Hojkg2YNyaZ7mtC/BjXYiiX+WOSEBceLNhPUMg7cRea6CiUHLngpWZQcUdYGcXNE+JdYkNJB3SJFewvDBHB5r5I6n1N9S0I8R1iJyaPpCeKus8DtBrMH5OEJ9cf5XwtQDkh79SXqQtNdDxMzA3Cl95VasgXO8B5/OrBQSEUVkaIe3nrA1JsKCnfhIhV12TCrgKj5D5Crj6NEKIMcp/FzcovwcIdJzi/pqSxiZx9GU2YPEPDMIy706W7TK1VlG2JuUH48sizb32peeSz8kscTnRmDr8BCXFhdJMR0dR4PyqhzUoY7ItdaHn2859QyVE7y5l+SO4+jaifEu5HZ6i13e4kR7/G10ew3pzUB5k9vd9HyNmXKeHzQO3E3o800fEQMTfIiGQDBi7J4c18xG4F75+dIWpCwqaXluv1CAHUeT96u81qGuw3mxikL86Bsdr1foP6IMLF2/ejs9TabndzZWdCLX2EnO1U0+eBkom9H7UebJPfEiqEBbQUwjookN7VMo+8GFLy0hNC3EPs/d9sUsaa06Gict5JDiCt36A+iBDfx57Zm5DaHmldYiVNSNTSR8jVTrV9HvgCmuh4gNgbJPe3UlGvJ3e+ecpLT4j7qOWDnCVnv0F9ECHEEbX0EXK1U22fB76AJjoeIP4GFbcKIne+ecpLT4j7qOWDnCVnv0F9ECHEEbX0EXK1U22fB76AJjoeIPYGSbua3pVvuqNBy2E1sXnk2fSPcr0eIUQ6tXyQs+TsN6gPIoQ4opY+Qq52qu3zwBfQRMcDxN4gAzrH4vlxyebHbK8BpKVrZNM/yvV6YjSbGOQWlmFL3jnkFpZRnCnxe0r8IHd0n8rZb3ijDyLEl/j6Z6pa+gi52qnEzwNfR1nXPITNsgFw55u3zLIhd9pBT6UxpHSJ/kGN96O32yzl/vdEW8Tcp3Lez9Q3EEvevh+d5el2+9N9o5afVc502oD3Pw/UjNJLK5CUG0TuQlLuLkxF6RL9hxrvRyW0WQkf5FLvUzn7DSqOR1hKuB+d4cl2++Nnqlr6CDnaqYTPA7WjiY5CqeVGlkItefCJPNR4Pyqlzd68/+k+JUqhlPtRKk+1m+5V/+CL40FPEns/BnqwTQTX8s37EinpEn3tZydECm/e/3SfEqIOdK/6B18cDyoRJSMgLqN0iYQoH92nhKgD3auEyIcmOsRllC6REOWj+5QQdaB7lRD5UOiaAqg9TpNNl2isqrM7OAlciyemdImEuB9ff0L3KSHq4Kv3qtrHOkSdaKLjZb6QeYPNL//EuiPQgDtdohLy4BPi64T6E7pPCVE+X/xM9YWxDlEnCl3zIjZ9pO2hQ2NVHZ5YdwRZ+SVOv7ani4yNSonHygf6wBBpvZVuiAzxyTSYhLhK7ntUTH9C9ykh6uBL96o7xzpS+XoBVmKPdnS8pNnE4IVtBZzb0gxaVm1e2FaAEckG1eRnH5USjxHJBtqaJkSA3PeolP6E7lNC1MEX7lV3jnWkol0l/0Q7Ol4iJX2kFN5eOWHTJU5IbY+0LrGq6pAJ8QR33KNS+xO6TwlRB7Xfq+4a60jl7bER8R6nJjpvvvkmEhMTERISgr59++Lbb791eP3evXvRt29fhISEoHPnzli1apVTjfUl7kgfKbRyArSsnNBWLfE2f+1D3HWPUjpa4o/8tR9REyX0TTQ28m+SJzobN27EjBkzMG/ePBw9ehSDBg3C6NGjcfbsWc7ri4qKkJmZiUGDBuHo0aOYO3cunn76aWzatMnlxquZO9JHKmXlhBBH/LkPcdc9Suloib/x535ETZTQN9HYyL9Jnuj83//9HyZPnoy//vWvSEpKwrJly9ChQwesXLmS8/pVq1ahY8eOWLZsGZKSkvDXv/4Vjz32GF599VWXG69mbPpIvk1oDVpiR6Wkj1TCygkhQvy5D3HXPeqO/oQQJfPnfkRNlNA30djIv0ma6DQ0NODw4cO47bbbrB6/7bbb8N1333E+Jzc31+76kSNH4scff0RjYyPnc+rr61FdXW313/+3d+fhTVX5/8Df6ZYutoG2lqQsbWFwKWFHpLLJjiDgfJ1FFNz5DQjIMs4go34Rl0FnHGUcFUe/iIMVcBQUEKZDkW3AYkdKhVoVqIViSURaSLDQNef3R72hababNGmSm/frefpob86999ySm+d+cs75fJRGSh8JwO4DwNv0kcHwzQmRK+H+GeKve9QfnydEwSrcP0dCSTB8NvHZKLx5FOicO3cOTU1N6NSpk832Tp06wWg0OtzHaDQ6bN/Y2Ihz58453GfFihXQaDTWn65du3rSzZDh6/SRwfDNCZEr4f4Z4s97VEnpaIlcCffPkVAT6M8mPhuFN6/SS6tUtm8XIYTdNnftHW2XLF26FIsXL7b+bjabFfsB48v0kUosMkbKFK6fIf6+R5WQjpZIrnD9HAlFgfxs4rNRePMo0ElNTUVkZKTdNyZnz561+6ZEotVqHbaPiopCSkqKw33UajXUarUnXQtpUvpIX5C+OWmdK17LXPEUBPgZ4v971JefJ0TBiJ8joSmQn018NgpfHgU6MTExGDhwIPLz8/Hzn//cuj0/Px/Tpk1zuE9OTg62bt1qs23Hjh0YNGgQoqOjvegyucNvdSlY8TOkGe9RIu/xc4S8wc/dMCU8tGHDBhEdHS1Wr14tSktLxcKFC0VCQoI4efKkEEKIRx99VMycOdPa/ttvvxXx8fFi0aJForS0VKxevVpER0eLDz74QPY5TSaTACBMJpOn3SUiH2vr/cjPEKLw5ov7kZ8jROFN7v3o8RqdX//616iqqsJTTz0Fg8EAvV6P7du3IyMjAwBgMBhs8thnZWVh+/btWLRoEV599VWkp6fj5Zdfxu233+6LOI2IQgw/Q4iorfg5QkRyqIQQQV8K1mw2Q6PRwGQyISkpKdDdIQproXg/hmKfiZQqVO/HUO03kRLJvR89LhhKREREREQU7BjoEBERERGR4jDQISIiIiIixWGgQ0REREREisNAh4iIiIiIFIeBDhERERERKY7HdXQCQcqAbTabA9wTIpLuwxDITG/FzxCi4BGKnyEAP0eIgoncz5GQCHQuXrwIAOjatWuAe0JEkosXL0Kj0QS6G7LwM4Qo+ITSZwjAzxGiYOTucyQkCoZaLBacOXMGiYmJUKlUHu1rNpvRtWtXnD59OqQKfLHf7S9U+97e/RZC4OLFi0hPT0dERGjMfm3LZ0gwCtX3qrd4vcoSip8hQGA/R5T8nuC1ha5AXp/cz5GQGNGJiIhAly5d2nSMpKSkkHyTsd/tL1T73p79DqVvYQHffIYEo1B9r3qL16scofYZAgTH54iS3xO8ttAVqOuT8zkSOl+lEBERERERycRAh4iIiIiIFEfxgY5arcayZcugVqsD3RWPsN/tL1T7Hqr9Ju+F2785r5fCnZLfE7y20BUK1xcSyQiIiIiIiIg8ofgRHSIiIiIiCj8MdIiIiIiISHEY6BARERERkeIw0CEiIiIiIsUJukBnxYoVuOGGG5CYmIi0tDTcdttt+Oabb2zaCCHw5JNPIj09HXFxcbj55pvx5Zdf2rSpq6vD/PnzkZqaioSEBEydOhXfffedTZvz589j5syZ0Gg00Gg0mDlzJi5cuGDTpqKiAlOmTEFCQgJSU1Px8MMPo76+XtZ1qFQqLFy4MOj7XVlZiRkzZiAlJQXx8fHo168fDh06FNT9bmxsxOOPP46srCzExcWhe/fueOqpp2CxWIKu3/v27cOUKVOQnp4OlUqFjz76yGbfYOmn5OjRoxg5ciTi4uLQuXNnPPXUU2DOkuDj6DNGadx9NimNnM81Cm9Ku++Veo8r6V72xTNMQIkgM2HCBLFmzRpRUlIiiouLxeTJk0W3bt3Ejz/+aG3z3HPPicTERLFx40Zx9OhR8etf/1rodDphNputbWbPni06d+4s8vPzRVFRkRg1apTo27evaGxstLaZOHGi0Ov14tNPPxWffvqp0Ov14tZbb7W+3tjYKPR6vRg1apQoKioS+fn5Ij09XcybN8/lNRQWForMzEzRp08fsWDBgqDud3V1tcjIyBD33nuv+Oyzz0R5ebnYuXOnOHHiRFD3+5lnnhEpKSni448/FuXl5eL9998XV111lVi5cmXQ9Xv79u3iscceExs3bhQAxIcffmhzLcHSTyGEMJlMolOnTuKOO+4QR48eFRs3bhSJiYnihRdesPs3oMBx9hmjJHI+m5RGzucahS+l3fdKvseVdC/74hkmkIIu0Gnt7NmzAoDYu3evEEIIi8UitFqteO6556xtamtrhUajEa+//roQQogLFy6I6OhosWHDBmubyspKERERIfLy8oQQQpSWlgoA4uDBg9Y2BQUFAoD4+uuvhRDN/7gRERGisrLS2mb9+vVCrVYLk8nksL8XL14UPXv2FPn5+WLkyJHWD6Ng7feSJUvEsGHDHF5LMPd78uTJ4v7777fZ9j//8z9ixowZQd3v1h8SwdbP1157TWg0GlFbW2tts2LFCpGeni4sFougwHP2GaM07j6blMjd5xqFLyXe90q+x5V6L3vzDBNoQTd1rTWTyQQASE5OBgCUl5fDaDRi/Pjx1jZqtRojR47Ep59+CgA4dOgQGhoabNqkp6dDr9db2xQUFECj0eDGG2+0thkyZAg0Go1NG71ej/T0dGubCRMmoK6uzunQ6ty5czF58mSMHTvWZnuw9nvLli0YNGgQfvnLXyItLQ39+/fHm2++GfT9HjZsGD755BMcO3YMAPDFF19g//79mDRpUlD3u7Vg62dBQQFGjhxpU/xrwoQJOHPmDE6ePOnyWqh9OPuMURp3n01K5O5zjcKXEu97Jd/j4XIvy3mGCbSoQHfAFSEEFi9ejGHDhkGv1wMAjEYjAKBTp042bTt16oRTp05Z28TExKBjx452baT9jUYj0tLS7M6ZlpZm06b1eTp27IiYmBhrm5Y2bNiAoqIi/Pe//7V7LVj7/e2332LVqlVYvHgx/vCHP6CwsBAPP/ww1Go17r777qDt95IlS2AymXDdddchMjISTU1NePbZZzF9+nTrsYKx360FWz+NRiMyMzPtziO9lpWV5fJ6yL9cfcYojbvPJiVy97lG4Ump972S7/FwuZflPMMEWlAHOvPmzcORI0ewf/9+u9dUKpXN70IIu22ttW7jqL03bQDg9OnTWLBgAXbs2IHY2FinfQi2flssFgwaNAh//OMfAQD9+/fHl19+iVWrVtl80ARbv9977z3k5uZi3bp16NWrF4qLi7Fw4UKkp6fjnnvuCdp+OxNM/XTUF2f7UvuR+xmjFHI/m5RE7ucahQ8l3/dKvsfD7V725hmmvQTt1LX58+djy5Yt2L17N7p06WLdrtVqAcDum/KzZ89aI0qtVov6+nqcP3/eZZvvv//e7rw//PCDTZvW5zl//jwaGhrsotdDhw7h7NmzGDhwIKKiohAVFYW9e/fi5ZdfRlRUlM234sHUb51Oh+zsbJtt119/PSoqKqzHCsZ+/+53v8Ojjz6KO+64A71798bMmTOxaNEirFixIqj73Vqw9dNRm7NnzwKw/8aG2pe7z5impqZAd9Gn3H02KZG7zzUKP0q+75V8j4fLvSznGSbQgi7QEUJg3rx52LRpE3bt2mU3VSYrKwtarRb5+fnWbfX19di7dy9uuukmAMDAgQMRHR1t08ZgMKCkpMTaJicnByaTCYWFhdY2n332GUwmk02bkpISGAwGa5sdO3ZArVZj4MCBNv0aM2YMjh49iuLiYuvPoEGDcNddd6G4uBjdu3cPyn4PHTrULn33sWPHkJGREdR/70uXLiEiwvbtGxkZaU3dGKz9bi3Y+pmTk4N9+/bZpJzesWMH0tPT7aa0Ufty9xkTGRkZ6C76lLvPJiVy97lG4UfJ972S7/FwuZflPMMEXPvlPZBnzpw5QqPRiD179giDwWD9uXTpkrXNc889JzQajdi0aZM4evSomD59usN0vF26dBE7d+4URUVFYvTo0Q7T8fbp00cUFBSIgoIC0bt3b4fpeMeMGSOKiorEzp07RZcuXdyml5a0zowSjP0uLCwUUVFR4tlnnxXHjx8X7777roiPjxe5ublB3e977rlHdO7c2Zq6cdOmTSI1NVX8/ve/D7p+X7x4URw+fFgcPnxYABAvvviiOHz4sDh16lRQ9VOI5gxvnTp1EtOnTxdHjx4VmzZtEklJSUwvHaSUkn3JETmfTUoj53ONSCn3vZLvcSXdy754hgmkoAt0ADj8WbNmjbWNxWIRy5YtE1qtVqjVajFixAhx9OhRm+NcvnxZzJs3TyQnJ4u4uDhx6623ioqKCps2VVVV4q677hKJiYkiMTFR3HXXXeL8+fM2bU6dOiUmT54s4uLiRHJyspg3b55N6l1XWn8YBWu/t27dKvR6vVCr1eK6664Tb7zxhs3rwdhvs9ksFixYILp16yZiY2NF9+7dxWOPPSbq6uqCrt+7d+92+J6+5557gqqfkiNHjojhw4cLtVottFqtePLJJ5laOkgp5YHHGXefTUoj53ONSEn3vVLvcSXdy754hgkklRAseU5ERERERMoSdGt0iIiIiIiI2oqBDhERERERKQ4DHSIiIiIiUhwGOkREREREpDgMdIiIiIiISHEY6BARERERkeIw0CEiIiIiIsVhoENERERERIrDQIeIiIiIiBSHgQ4RERERESkOAx0iIiIiIlIcBjpERERERKQ4DHSIiIiIiEhxFBvovP3221CpVNafqKgodOnSBffddx8qKytlHePee+9FZmamfzvawqJFi6BSqfD11187bfPYY49BpVKhqKgIAFBTU4Pnn38effv2RVJSEhITE9GjRw/86le/wt69e2Wf+9y5c1Cr1VCpVPj888/bfC3+4It/U39ry3tm3bp1WLlypcPXVCoVnnzySa/7RURERBRuFBvoSNasWYOCggLk5+dj1qxZWL9+PYYPH46amhq3+z7xxBP48MMP26GXzR544AEAwFtvveXwdYvFgrVr16Jfv34YMGAAmpqaMH78eDz77LP4xS9+gffffx8ffPABFi1aBJPJhP/85z+yz/3OO++gvr4eALB69eq2X4wfteXfNJi5CnQKCgrw4IMPtm+HiIiIiEJYVKA74G96vR6DBg0CAIwaNQpNTU14+umn8dFHH+Guu+5yuM+lS5cQHx+PHj16tGdXodfrMXjwYLzzzjv44x//iKgo23+eHTt24LvvvsOSJUsAAPv27cOnn36Kt956C/fdd5+13YQJEzBv3jxYLBbZ537rrbeQlpaGjIwMrF+/Hi+++CLi4uLc7if9rdqTN/+moW7IkCGB7gIRERFRSFH8iE5r0gPjqVOnADRPNbrqqqtw9OhRjB8/HomJiRgzZoz1tdbTkCwWC/72t7+hX79+iIuLQ4cOHTBkyBBs2bLFpt17772HnJwcJCQk4KqrrsKECRNw+PBht/174IEHYDQa8a9//cvutTVr1kCtVlsf5quqqgAAOp3O4bEiIuT983722WcoKSnBzJkzMWvWLJhMJmzcuNGu3c033wy9Xo99+/bhpptuQnx8PO6//34AgNlsxiOPPIKsrCzExMSgc+fOWLhwod0oy6uvvooRI0YgLS0NCQkJ6N27N/70pz+hoaFBVl8daf1vWltbi6VLl9r0Ze7cubhw4YLNfpmZmbj11lvx4Ycfok+fPoiNjUX37t3x8ssv27STpsydPHnSZvuePXugUqmwZ88el/2Tc80333wztm3bhlOnTtlMz5M4mrpWUlKCadOmoWPHjoiNjUW/fv3wj3/8w2Ef169fj8ceewzp6elISkrC2LFj8c0337jsNxEREVEoC7tA58SJEwCAq6++2rqtvr4eU6dOxejRo7F582YsX77c6f733nsvFixYgBtuuAHvvfceNmzYgKlTp9o8BP/xj3/E9OnTkZ2djX/+85945513cPHiRQwfPhylpaUu+zd9+nTEx8fbTV87f/48Nm/ejJ///Ofo2LEjAGDQoEGIjo7GggUL8O6778JgMHj65wBwZara/fffjzvuuAPx8fFOp68ZDAbMmDEDd955J7Zv346HHnoIly5dwsiRI/GPf/wDDz/8MP71r39hyZIlePvttzF16lQIIaz7l5WV4c4778Q777yDjz/+GA888AD+/Oc/4ze/+Y1XfQds/02FELjtttvwwgsvYObMmdi2bRsWL16Mf/zjHxg9ejTq6ups9i0uLsbChQuxaNEifPjhh7jpppuwYMECvPDCC173pzU51/zaa69h6NCh0Gq1KCgosP4488033+Cmm27Cl19+iZdffhmbNm1CdnY27r33XvzpT3+ya/+HP/wBp06dwv/93//hjTfewPHjxzFlyhQ0NTX57DqJiIiIgopQqDVr1ggA4uDBg6KhoUFcvHhRfPzxx+Lqq68WiYmJwmg0CiGEuOeeewQA8dZbb9kd45577hEZGRnW3/ft2ycAiMcee8zpeSsqKkRUVJSYP3++zfaLFy8KrVYrfvWrX7nt+z333COio6PF999/b932t7/9TQAQ+fn5Nm1Xr14trrrqKgFAABA6nU7cfffdYt++fW7PI4QQNTU1IikpSQwZMsTm/CqVSpw4ccKm7ciRIwUA8cknn9hsX7FihYiIiBD//e9/bbZ/8MEHAoDYvn27w3M3NTWJhoYGsXbtWhEZGSmqq6td9lXOv2leXp4AIP70pz/Z7Pvee+8JAOKNN96wbsvIyBAqlUoUFxfbtB03bpxISkoSNTU1NuctLy+3abd7924BQOzevdu6rfV7xpNrnjx5stN9AYhly5ZZf7/jjjuEWq0WFRUVNu1uueUWER8fLy5cuGDTx0mTJtm0++c//ykAiIKCAqd9JSIiIgplih/RGTJkCKKjo5GYmIhbb70VWq0W//rXv9CpUyebdrfffrvbY0nTyebOneu0zb///W80Njbi7rvvRmNjo/UnNjYWI0eOdDvNCWievtbQ0IB33nnHum3NmjXIyMiwTquT3H///fjuu++wbt06PPzww+jatStyc3MxcuRI/PnPf3Z7rn/+858wm83WKWjSMYUQWLNmjV37jh07YvTo0TbbPv74Y+j1evTr18/mmidMmGA3tevw4cOYOnUqUlJSEBkZiejoaNx9991oamrCsWPH3PYXcP1vumvXLgDNI28t/fKXv0RCQgI++eQTm+29evVC3759bbbdeeedMJvN1sx2beWLa25t165dGDNmDLp27Wqz/d5778WlS5fsRoOmTp1q83ufPn0AXJnuR0RERKQ0ik9GsHbtWlx//fWIiopCp06dHK5niY+PR1JSkttj/fDDD4iMjIRWq3Xa5vvvvwcA3HDDDQ5fl7NuZvjw4bjmmmuwZs0a/Pa3v8WRI0dQVFSEJ5980mbdhkSj0WD69OmYPn06AODLL7/E2LFj8dhjj2HWrFno0KGD03OtXr0asbGxmDhxonUNS58+fZCZmYm3334by5cvR2RkpLW9o7/f999/jxMnTiA6OtrhOc6dOwcAqKiowPDhw3Httdfir3/9KzIzMxEbG4vCwkLMnTsXly9fdvu3AVz/m1ZVVSEqKspmaiLQvMZFq9Va1zVJHP1bSttat/WGr665taqqKof/Funp6dbXW0pJSbH5Xa1WA4DX5yciIiIKdooPdK6//nprhi5nHAUPjlx99dVoamqC0Wh0mgAgNTUVAPDBBx8gIyPDs862cP/99+PRRx9FYWEh1q1bh4iICLtRCmd69eqFO+64AytXrsSxY8cwePBgh+2OHTuG/fv3AwC6devmsM2///1vTJo0yfq7o79Vamoq4uLinKbFlv4mH330EWpqarBp0yabv01xcbGs65K4+jdNSUlBY2MjfvjhB5tgRwgBo9FoF4AajUa7Y0jbpOAgNjYWAOzW90gBnCu+uubWUlJSHK7JOnPmDIArf3MiIiKicKX4qWu+dMsttwAAVq1a5bTNhAkTEBUVhbKyMgwaNMjhjxz33HMPoqKi8Pe//x3vvvsuxowZYxc4VVVVWWvftCYVHZW+4XdESjjw5ptvYvfu3TY/27dvR3R0tNPgpaVbb70VZWVlSElJcXi9UuY6KUiSRhOA5gDkzTffdHsOuaSpfbm5uTbbN27ciJqaGrupf19++SW++OILm23r1q1DYmIiBgwYAADW/h85csSmXetMe454cs1qtVr2CMuYMWOwa9cua2AjWbt2LeLj45mOmoiIiMKe4kd0fGn48OGYOXMmnnnmGXz//fe49dZboVarcfjwYcTHx2P+/PnIzMzEU089hcceewzffvstJk6ciI4dO+L7779HYWEhEhISXGZ1k2i1WkyaNAlr1qyBEMJaTLSl3bt3Y8GCBbjrrrtw0003ISUlBWfPnsX69euRl5eHu+++G126dHF4/MbGRusUMGeFKKdMmYItW7bYjY60tnDhQmzcuBEjRozAokWL0KdPH1gsFlRUVGDHjh347W9/ixtvvBHjxo1DTEwMpk+fjt///veora3FqlWrcP78ebd/D7nGjRuHCRMmYMmSJTCbzRg6dCiOHDmCZcuWoX///pg5c6ZN+/T0dEydOhVPPvkkdDodcnNzkZ+fj+eff95aH+iGG27Atddei0ceeQSNjY3o2LEjPvzwQ+tomLv+yL3m3r17Y9OmTVi1ahUGDhyIiIgIp4HxsmXL8PHHH2PUqFH43//9XyQnJ+Pdd9/Ftm3b8Kc//QkajcaLvx4RERGRggQ0FYIfSZmyWmcCa+2ee+4RCQkJTl9rnQWrqalJvPTSS0Kv14uYmBih0WhETk6O2Lp1q027jz76SIwaNUokJSUJtVotMjIyxC9+8Quxc+dO2dewefNmAUAkJyeL2tpau9dPnz4tHn/8cTF06FCh1WpFVFSUSExMFDfeeKP429/+JhobG50e+6OPPhIAxMqVK522kTKY/eUvfxFCNGdd69Wrl8O2P/74o3j88cfFtddea/279O7dWyxatMia4U4IIbZu3Sr69u0rYmNjRefOncXvfvc78a9//csue5kjcv9NL1++LJYsWSIyMjJEdHS00Ol0Ys6cOeL8+fM27TIyMsTkyZPFBx98IHr16iViYmJEZmamePHFF+2OeezYMTF+/HiRlJQkrr76ajF//nyxbds2WVnX5F5zdXW1+MUvfiE6dOggVCqVaHl7olXWNSGEOHr0qJgyZYrQaDQiJiZG9O3bV6xZs8amjZR17f3337fZXl5eLgDYtSciIiJSCpUQLYqcEIWRzMxM6PV6fPzxx4HuChERERH5GNfoEBERERGR4jDQISIiIiIixeHUNSIiIiIiUhyO6BARERERkeIw0CEiIiIiIsVhoENERERERIrDQIeIiIiIiBSHgQ4RERERESlOSAU6+/btw5QpU5Ceng6VSoWPPvrI42MIIfDCCy/gmmuugVqtRteuXfHHP/7R950lIiIiIqKAiQp0BzxRU1ODvn374r777sPtt9/u1TEWLFiAHTt24IUXXkDv3r1hMplw7tw5H/eUiIiIiIgCKWTr6KhUKnz44Ye47bbbrNvq6+vx+OOP491338WFCxeg1+vx/PPP4+abbwYAfPXVV+jTpw9KSkpw7bXXBqbjRERERETkdyE1dc2d++67DwcOHMCGDRtw5MgR/PKXv8TEiRNx/PhxAMDWrVvRvXt3fPzxx8jKykJmZiYefPBBVFdXB7jnRERERETkS4oJdMrKyrB+/Xq8//77GD58OHr06IFHHnkEw4YNw5o1awAA3377LU6dOoX3338fa9euxdtvv41Dhw7hF7/4RYB7T0REREREvhRSa3RcKSoqghAC11xzjc32uro6pKSkAAAsFgvq6uqwdu1aa7vVq1dj4MCB+OabbzidjYiIiIhIIRQT6FgsFkRGRuLQoUOIjIy0ee2qq64CAOh0OkRFRdkEQ9dffz0AoKKigoEOEREREZFCKCbQ6d+/P5qamnD27FkMHz7cYZuhQ4eisbERZWVl6NGjBwDg2LFjAICMjIx26ysREREREflXSGVd+/HHH3HixAkAzYHNiy++iFGjRiE5ORndunXDjBkzcODAAfzlL39B//79ce7cOezatQu9e/fGpEmTYLFYcMMNN+Cqq67CypUrYbFYMHfuXCQlJWHHjh0BvjoiIiIiIvKVkAp09uzZg1GjRtltv+eee/D222+joaEBzzzzDNauXYvKykqkpKQgJycHy5cvR+/evQEAZ86cwfz587Fjxw4kJCTglltuwV/+8hckJye39+UQEREREZGfhFSgQ0REREREJIdi0ksTERERERFJGOgQEREREZHihETWNYvFgjNnziAxMREqlSrQ3SEKa0IIXLx4Eenp6YiI4HclREREFJxCItA5c+YMunbtGuhuEFELp0+fRpcuXQLdDSIiIiKHQiLQSUxMBND8YJWUlBTg3hCFN7PZjK5du1rvSyIiIqJgFBKBjjRdLSkpiYEOUZDgNFIiIiIKZpxgT0REREREisNAh4iIiIiIFIeBDhERERERKQ4DHSIiIiIiUhwGOkREREREpDgeBTorVqzADTfcgMTERKSlpeG2227DN99843KfPXv2QKVS2f18/fXXbeo4ERERERGRMx4FOnv37sXcuXNx8OBB5Ofno7GxEePHj0dNTY3bfb/55hsYDAbrT8+ePb3uNBERERERkSse1dHJy8uz+X3NmjVIS0vDoUOHMGLECJf7pqWloUOHDh53kIiIiIiIyFNtWqNjMpkAAMnJyW7b9u/fHzqdDmPGjMHu3btdtq2rq4PZbLb5IeVqsggUlFVhc3ElCsqq0GQRge4SEREREYU4j0Z0WhJCYPHixRg2bBj0er3TdjqdDm+88QYGDhyIuro6vPPOOxgzZgz27NnjdBRoxYoVWL58ubddoxCSV2LA8q2lMJhqrdt0mlgsm5KNiXpdAHtGRERERKFMJYTw6uvzuXPnYtu2bdi/fz+6dOni0b5TpkyBSqXCli1bHL5eV1eHuro66+9msxldu3aFyWRCUlKSN92lIJRXYsCc3CK0fgOqfvrvqhkDGOwEIbPZDI1Gw/uRiIiIgppXU9fmz5+PLVu2YPfu3R4HOQAwZMgQHD9+3OnrarUaSUlJNj+kLE0WgeVbS+2CHADWbcu3lnIaGxERERF5xaNARwiBefPmYdOmTdi1axeysrK8Ounhw4eh0/Gb+nBWWF5tM12tNQHAYKpFYXl1+3WKiIiIiBTDozU6c+fOxbp167B582YkJibCaDQCADQaDeLi4gAAS5cuRWVlJdauXQsAWLlyJTIzM9GrVy/U19cjNzcXGzduxMaNG318KRRKzl50HuR4046IiIiIqCWPAp1Vq1YBAG6++Wab7WvWrMG9994LADAYDKioqLC+Vl9fj0ceeQSVlZWIi4tDr169sG3bNkyaNKltPaeQlpYY69N2REREREQteZ2MoD1x8bPyNFkEhj2/C0ZTrcN1OioAWk0s9i8ZjcgIlYMWFCi8H4mIiCgUtKmODpG3IiNUWDYlG8CVLGsS6fdlU7IZ5BARERGRVxjoUMBM1OuwasYAaDW209O0mlimliYiIiKiNvG6YCiRL0zU6zAuW4vC8mqcvViLtMRYDM5K5kgOEREREbUJAx0KuMgIFXJ6pAS6G0RERESkIJy6RkREREREisMRHQoKTRbB6WtERERE5DMMdCjg8koMWL61FAbTleKgOk0slk3JZkICIiIiIvIKp65RQOWVGDAnt8gmyAEAo6kWc3KLkFdiCFDPiIiIiCiUMdChgGmyCCzfWuqwYKi0bfnWUjRZgr6mLREREREFGQY6FDCF5dV2IzktCQAGUy0Ky6vbr1NEREREpAhco0MBc/ai8yDHm3b+xoQJRERERKGDgQ4FTFpirE/b+RMTJhARERGFFk5do4AZnJWMDvHRLtt0jI/G4Kxkn52zySJQUFaFzcWVKCirkrX+hwkTiIiIiEIPR3QoqPkyDYE3ozLuEiao0JwwYVy2ltPYiIiIiIIIR3QoYArLq3HhUoPLNhcuNfgkGYG3ozLeJEzwZtSIiIiIiHyLIzoUMO2VjKAtozKe9tEXa3mY9ICIiIio7RjoUMC0VzICT0ZlcnqkeHXutMRY66hR64BKGjVaNWOA22CHSQ+IiIiIfINT1yhgBmclQ6eJhbOxChWaH/LbmoygLSNHg7OSoU1SO91H6uPAjI5tLn7KpAdEREREvsNAhwImMkKFZVOyAcAu2JF+XzYlu83TtrwZOZLW2Ty7rRQ/1jU5bN+yj4dOnW9T8VN30+sA94ESEREREV3BQIcCaqJeh1UzBkCrsQ1GtJpYWVO95PB05CivxIBhz+/C9DcP4q0DJ/FjXaPD/TTx0dY+tnW9kTdJD4iIiIjIOa7RoYCbqNdhXLbWbwvwIyNUeGJyNh5aV2T3WuuRI2frbByJi47EuGwtgLavN2qvxAxERERE4YKBDgWFyAiVXSIAX2iyCLyy6wTWHCh3+Lq2xUJ/V9PHHGmZwEAaNTKaah3ur/rpXM7WG7VXYgYiIiKicMFAh4KGr9Mq55UY8Oimoy5r9Twx+Xrr9Dh308cckUZYpPVGc3KLoIJtoVM5643aGigRERERkS0GOhQUfJ1WOa/EgNm59lPVWlIBeHrbVxibrcWhU+fxLy+ymrUcYZHWG7W+Dq2M62hroEREREREtlRCiKBP42Q2m6HRaGAymZCUlBTo7pCPOVsXIz3Se5qUoMkiMOz5XbJHZxJjo3Cx1nHCAWekEZb9S0bbBR9tGZkKhTo6vB+JiIgoFHBEhwLKXVplFZrTKo/L1soOFjydguZNkAM4H2Fpy3ojfydmICIiIgoXDHQooDxJqyw3ePB3ZjJNfDSe+5/efhth8VdiBiIiIqJwwkCHAsofaZX9nZnM5CC5gaPpagA4MkNEREQUIAx0KKBSE9Sy2nkSvLjLYOYLLafTOVpX0yE+GgBsMr4F21obIiIiIiWLCHQHKHzllRjw2/e/cNsuOSEaRnMtCsqq0GRxH7pIGcyAK+tpfKnldDopkULr6XcXLjXYpbU2mmoxJ7cIeV5kdyMiIiIizzDQoYCQAgSj2f2UtOqaBix6rxjT3zyIG57die1H3AcKUqpnrcZ/09iM5lqPCoxK7ZZvLZUVsBERERGR9zh1jdqdq0xr7lTX1OOhdUX4zXdZWDop22Xb1hnMUhPU+O37X+B7s2+mtFX/WOdxgVFvkisQERERkec4okPtztP0z478fV85th8547adlMFsWr/OGNozFU9O9c2UtggV0CE+xuv9/Z0ZjoiIiCjcMdChduerh/zHN5d4PAVsXLYWC8deA01ctM32xNhIj45jEcCFS/Ue7dOSvzPDEREREYU7BjrU7nz1kF9d04C3D5TLDnbySgwY9vwuvLTzGC5cbk4UoImLxi8GdMFt/Tp7fP7khBjoNLEejQ6p0Jx9TUo/TURERET+wUCH2p2U/tkXGdGe3vYVhj2/yyaTWZNFoKCsCpuLK62Z2pxlRzNdbsAHRd/hnYMVHp9bq4nzKLub1GbZlGzW0yEiIiLyMyYjoHYnpX+ek1sEFdDmxABS2uZVMwYAgF1NG21SLGobm3xWU0cFQKu5UgB01YwBdufsGB8NAds6OtogqKPjqLApgy4iIiJSIpUQIujz3JrNZmg0GphMJiQlJQW6O+QjjgptRqia1794Iz4mEpfqm3zUO8ekkGDVjAE2AYujAAJAUAUVjv7e3hQx5f1IREREoYCBDgVUfaMF7xScxKnqS8hIjkenRDXmbyj22eiLr3kTGEgCOZoiTd1r/Xd1Fri5wvuRiIiIQgGnrlHAOBthmNxHh49lFAX1t7tzMjChlxYQwLmaOq+DkyaLwCu7jmPNgZPWJAiA+6DJV4GRq7pFAs3BzvKtpRiXreU0NiIiIlIMBjokmy9HJJyNMBhNtUER5ADALXpdm4t65pUY8OimozZrdSQt1xa1DnYcBYHJCTF4Zpoek/p4Nprkrm4Ri5gSERGREjHQIVl8tb4DcD/CEGhSsoGBGR1RUFbldWDnLJiTOBtNcbZfdU09HlpXhN98l4Wlk7Jl90Nu3SIWMSUiIiIlYaBDbrkafXE2IuFq9MfdCEMgSWHM1L46jPzzbqeBnbvRLVfBXEutR1Pk7Pf3feXo26UDJvVJl3VNcusWsYgpERERKQkDHXLJm/Ud7kZ/gnnkQKuJxdS+Oryxr9xpYPf/RmRhyxcGl6NbngZzO0uNyOmRInu/xzeXYIJeJ2uESapbZDTVOvx3bJkum4iIiEgpWDCUXPJkfQcAp4U5pSAhr8QQlCMHHeKj8e4DN2Lv70ZhyxcGp4GdQPOIiqvrAzyfBrb6wEnklRhk71dd02D9m7sj1S0C7AubsogpERERKRUDHXLJk/UdctbeLN9aioEZHaHTxNo9dAfShUsNiIhQ4dCp815Nq2t5fU0W4VUwt3xrKVIT1LLbexJMTdTrsGrGAGg1tv3SamI9Si1NREREFCo4dY2sHK078WR9h9zRn0OnzmPZlGzMyS2CCsGRgABo+2L8lqNb7qaLOWIw1QKq5uxq1TX1btt7GkxN1OswLlsbVEVMiYiIiPyFgQ4BcL6u5onJ18te3/HxkTOyznX2Yi2m9euMVTMG2J0zkHw1pe7sxVrrdDFPg7lzP9bhmWl6PLSuyGU7nZdraiIjVEwhTURERGGBU9fI5bqauesOY2rf5mlN7tZ3eJrda6Jeh/1LRmPR2J5t6X6bqXAlcJBGYtoyxtHy+hxNF3O376Q+OvxmRJbL/nJNDREREZFrDHTCnJx1NVu+MODVO92v73AXJLQMKFra8N/TbbqGtmgdrMlZuO/qWK2vTwrm3n3wRnSIi3a5f8t9l07Kxmt39kdygu0+yQnRePXO/lxTQ0REROQGA50wJ3ddTceEGOxfMhrrZw3BX+/oh/WzhmD/ktE2D9zeZPcKdE2djgnRuH9oJjRxMWiyNId2rhbu/2ZEFlTwLHtZZIQKQ3+Wiudu7+1wX8nlhibklxqtv0/qk45npvVGckKMdVt1TQOe3vaVNbsbERERETmmEkIEy1pwp8xmMzQaDUwmE5KSkgLdHUXZXFyJBRuK3bb76x39MK1fZ6evt0xkcPJcDdYXVsBorrO+3rrOjOTDou+w6J9feN1/bzwx+XpUXriMj4rP2Cz61ybFYvrgbshMjUdaYiwGZnTEf09Wo6CsCoBATvdUDOmRgvxSo8s6Qa7klRjw6KajuHCpwe41KQCSRsmcFWpt3a698X4kIiKiUMBkBGHO03U1jmw/YsDjm0vsgoZFY6+xBg1Sdi/bgOgS/vHpybZegmxS4oROibF4ettXdq8bzbV4aecx6+8JMZFQqYAf65oAAK/sLrMGNPuXjPYqe9m4bC2e3PKlw9daFmBNiI7CoxuPelSolYiIiIiu4IhOmGuyCAx7fpfbrGr7l4x2+EC9Ynsp/r6v3OGxVbAddXCU2a29SJnPRl17NfYc+wHevuvbOppSUFaF6W8e9O7kDqyfNaTds6jxfiQiIqJQ4NEanRUrVuCGG25AYmIi0tLScNttt+Gbb75xu9/evXsxcOBAxMbGonv37nj99de97jD5ljfraoDmAOml/GNOgxygObCQCmg6y+zWXlQ/dX/3N94HOUDzNQkAT2750rqmxxNtrdXj7+MRERERKYVHgc7evXsxd+5cHDx4EPn5+WhsbMT48eNRU1PjdJ/y8nJMmjQJw4cPx+HDh/GHP/wBDz/8MDZu3NjmzpNvuFp872jkIq/EgKHPfYK/fnLc7bENploc/LbKaWY3f5o5pBtuviYVAOBFTOKS0VyHV3adsNnWZBEoKKvC5uJKFJRVOQyEfFWrR3LynPN7j4iIiCictWnq2g8//IC0tDTs3bsXI0aMcNhmyZIl2LJlC7766sqaiNmzZ+OLL75AQUGBrPNwqkz7aLl+xtm6E2cL5F2ZN+pneGX3CfcNfUilQptGbuR6vUXiADkJCuobLbjuiX/5LPDqlBiDT5eObdd1OrwfiYiIKBS0Kb20yWQCACQnO6/QXlBQgPHjx9tsmzBhAj7//HM0NNhnngKAuro6mM1mmx/yv8gIFXJ6pGBav87I6ZHicLqadyMz7b8MrL1Wni3fWoqX8o9htpOCq3Nyi2xSQR86dd6no0vfX6y3G1kiIiIiojYEOkIILF68GMOGDYNer3fazmg0olOnTjbbOnXqhMbGRpw7d87hPitWrIBGo7H+dO3a1dtukg95U/MmKTYKN2a272L59mQw1TqdwifFM9I6JcA/a2pe2nmMdXWIiIiIWvE60Jk3bx6OHDmC9evXu22rUtmODEiz5VpvlyxduhQmk8n6c/r0aW+7ST7kzUO6ubYR89877LRIptJJBVcLy6sB+G9NTctgioiIiIi8rKMzf/58bNmyBfv27UOXLl1cttVqtTAajTbbzp49i6ioKKSkOP6mX61WQ61We9M18iNvF9I7Ko4Zbs5erEVeiQEv7XSfwMEbUjDV3qmmiYiIiIKVR4GOEALz58/Hhx9+iD179iArK8vtPjk5Odi6davNth07dmDQoEGIjo72rLckm5zEAp4eJzk+xlqPhjyTmqDGb9//QlbbDvHRXgWHTDVNREREdIVHgc7cuXOxbt06bN68GYmJidaRGo1Gg7i4OADN084qKyuxdu1aAM0Z1l555RUsXrwYs2bNQkFBAVavXi1ryht5R24GMG+OQ56RCq7+92Q1jGb3f8dFY6/BvNE/swlSP/u2CitlpPL2depqIiIiolDm0RqdVatWwWQy4eabb4ZOp7P+vPfee9Y2BoMBFRUV1t+zsrKwfft27NmzB/369cPTTz+Nl19+GbfffrvvroKsnBXmdJQBzJvjkOem9tXJClQAwHy53i773fwxPaFNch7EqNAcyA7Ocp79kIiIiCjctKmOTnth3Q55miwCw57f5TI46RAXjVfvGoAh3e3TR3tyHHKvY3w0nr1Nj+VbS/H9xTrZ+73upEjrnNwiALZTB6V/QUeFXf2F9yMRERGFgjbV0aHgIif984XLDbjr/z7DsOd3OR3d8SaNNNmzCGB7idGjIAdwnEFtXLYWC8deA02c7bo2rSa2XYMcIiIiolDBQEdBPFmM7moqGxe1+4bpcgM+PuJ5fRuDqRZvHyi3Bjt5JQYMe34XXtp5DBcuNycp6BAXjUVje2L/ktEMcoiIiIgc8Cq9NAUnTxajCzRPe1q+tRTjsrU209hSr2Jq70B7ettXePM/5RiU2dFhsGS63ICVO4/jWm0iAx0iIiIiBziioyCDs5Kh08TKLs7Zupgl0Dx68Nt/FrvcT1r8/u4DN+KlX/VFckKMy3PqNLF4+Vd9kRgbKbNnBABGc63TESFpYhsLhRIRERE5xkBHQSIjVFg2JRsAZAc7wJWpatKCd6PZ9ZoSAWCSXouICBWm9uuMP/5c7/Ccqp9+pvbVYcW/v8HF2iYPekXuOApUiYiIiKgZAx2FmajXYdWMAdBq5E9jS0uMRZNFYPnWUrfFQKVgZvWBk5j+5kEMfe4TAHB4Tq0mFv9vRBbe2FfO5AZ+xDVVRERERPa4RkeBJup1GJetxcGyKsxdV2RdwN6aVMxycFay7ExrrQMho7kOs3OL8PqMAdi/ZLRNocuBGR0x8s+73QZP1DYnz10KdBeIiIiIgg5HdBQqMkKFoT1T8dztva1TyFqSfl82JRuREao2jwo8uukoANgUujx06jxHctrByp3HZBeCJSIiIgoXDHQUztlUttb1VzzJ2ObIhUsNOFhWZbONU6raD5MSEBEREdni1LUwIE1lazmtbHBWsk1K6cFZyegQF+10mpsc7x86jYJvqwAI5HRPZZrqdtIyKUFOj5RAd4eIiIgoKDDQCRORESqXD8GRESrcNzQTL+087vU5Pio+Y/3/V3aXIT4mAioVIDjQ0C44gkZERER0BaeukdW80T2RoPZdrZtL9RYGOe3o5LmaQHeBiIiIKGgw0CGryAgV/nx7n0B3g7z00s7jTEpARERE9BMGOiGqySJQUFaFzcWVKCir8tlC9El90vGbEVk+ORa1LxWYlICIiIhIwjU6ISivxIDlW0ttUjfrNLFYNiXbmkXNG00WgcLyamSna3Brby22HTWyBk4IYVICIiIioisY6ISYvBID5uQW2RfuNNViTm6RNWW0FLQ4y7IGwKbNyXOXsL6wAkYzF7SHOiYlICIiImKgE1KaLAKPbjrqcJRF4MrUJYsFeHqb6xEfR6NCpAxtrYlEREREpAQMdELIK7uO48Il53VupKlLD60rsnut5YgPAIejQhTaVGguBDs4KznQXSEiIiIKOAY6IaLJIrDmwEmv92854iOEYJCjMNKkxGVTsu2mKLojZ5ojERERUahhoBMiCsurceGy89EcOaQRH1IerZfJKPyV2IKIiIgo0JheOkRwgTk588Tk67F/yWivgpw5uUV2wa80zZE1eYiIiCiUcUQnRHCBOTmi08Ti3qFZsqaatZyilpqgxpNbSt0mthiXreU0NiIiIgpJDHRCxOCsZOg0sTCaar1eX6MC0CE+GhcuNXCNjkLIXZOz/cgZPL65BNU18qY/siYPERERhTpOXQsRkREqLJuSDeDKwnNPCQDnGeQoQof4aLz+U80kd1ZsL8VD6w7LDnJa4pRJIiIiClUMdELIRL0Oq2YMgFbj3TQ2TkBShoEZHXDo8XGygpztRwz4+75yr8/FKZNEREQUqhjohJiJeh32LxmNuaN6eLwvR3KU4dCpC/h3idFtuyaLwOObS7w+T8f4aNbkISIiopDFQCcERUaokBwf47fj3z4g3W/HJt/4/cYjaLK4Dl0Ly6tRXVPv9TkYGBMREVEoY6ATojrERfvt2J989YPfjk2+8WNdI17Zddxlm7aur7lwqQGF5dVtOgYRERFRoDDQCVFtLR4aqGOHq5lDuvn8mGsOnHQ5quOL9TVMRkBEREShioFOiEq+Sh3oLpBMEarmH1+7cNn1iIuUkrwtmIyAiIiIQhUDnRClTeIDaKiwCOC9z0/75diuRlyklOTexlgRKmBgRkcv9yYiIiIKLAY6IcoX39ZT+6lt8M/SfmnEpckiUFBWhc3FlSgoq7JOaZNSkrd+ryTFuq8VbBHAoVPnfd9pIiIionbg/mmHfK7JIlBYXo2zF2uRlhiLwVnJsqrbtyR9Wz8nt4jZscKQCoBW0/zeySsxYPnWUhhMV0Z3dJpYLJuSjYl6HSbqdRiXrbV5zxlNl7Hon1+4PQ/X6BAREVGoYqDTztw9lHpiol6H/zciC2/+pxwt16SrAEzuo4MQFmw7+r2Pek7BZtmUbOSXGh0Gu0ZTLebkFmHVjAGYqNchMkKFnB4p1tcLyqpknYNrdIiIiChUMdBpB9IITn6pEW8dOGn3euuHUkf7HyyrQsG35wA0P7AO6Z6C/FIj3thX7nBEZ9sRA6Ij/bACnoLC/xuRhXHZWgx7fpfDf3+B5oB3+dZSjMvW2o0YSlMfjaZah/u3HDEiIiIiCkUMdPzM0QhOa64eSvNKDHh001FcuHQl5fMru09AExcFlUrl9CEXAOqbOKlNqbZ8YcCIa9Lcvq8MploUllfbjOYAtlMfVbAtDiq9+5ZNyfZ4SiURERFRsGAyAj/KKzFgTm6Ry4dRScuH0pb7z84tsglyJKbLjQ63U3gwmGplTz9ztM6mySKgiYvB/UMz0THBtvisVhPrdHSRiIiIKFRwRMdPmiwCy7eWepwoQHoobbIIPLnlS993jBRE3rur9TobR6OMyQkxuK1fOsZla71KjkFEREQUbDii4yeF5dWyRnJakx5KC8urYTTX+bpbpCA53VOh08Q6rZOjQnOii5brbJyNMp6vqceaAydhulzPIIeIiIgUgYGOn3ialrf1QynT+pIz0ntlSI8ULJuSbd3Wug1gu87G1SijtG351lJrDR4iIiKiUMZAx088Scvr6KGUaX3JkdbvFakgaKcktU27Tklqu3U27kYZHa0TIyIiIgpVDHT8RErfK2cSkKPF34OzkqFt9fBKpImPdpIowNmYzhVyRwk5mkhERERKwEDHT6T0vYCjR85mDwzNxPpZQ7B/yWi7B9fICBX+99Zefu4lhRpTq0x70pobo9k2OPne3FybKa/EYN0md5SQo4lERESkBAx03GiyCBSUVWFzcSUKyqo8Wr8gTSvSamwfHHWaWLw+YwCemNILOT1SHC7+zisx4KmPmXUtHKmjXN+W0joaKTOf3DU37kYZHSUvICIiIgpVTC/tgqM0vDpNLJZNyZZdY2SiXodx2VoUllfj7MVapCXGuk3fK9XPofBU12hx+lrLdTTuMvO1LhjKIqFEREQUTjii44SzNLxGk/2UIF9qsgg8uumoX45NyrGz1IiXdh6T1bblmhtno4wsEkpERERKwxEdB9yl4VWheUrQuGyt22+/PR0VOlhWhQut1mEQtfZhcaXstq3X3HgzykhEREQUajii44Cv0vB6MypU8O05r/pM4UEFICUhBtU18oJhZ2tuIiNUyOmRgmn9OjtdJ0ZEREQUyhjoOOCLNLzeF2fkAye5Nq1fuuy2XHNDRERE4YqBjgO+SMPr7ahQTo8UWeem8JOSEINVMwZgXLZWVvtFY3tyzQ0RERGFLa7RcUBKw2s01TockVGhefG2qzS83o4KDemegg7x0VynQzaSE6JRsHQMYqIi0GQRLt+fQPOUtXmje7ZrH4mIiIiCCUd0HHBV7FNuGl5vR4UiI1R47n96y+0qhYk//rw3Yn6qr+Pu/akCp6wRERERMdBxoq1peNtSnHGiXodfDOjiZc9JyaQCtnWNFiwc2xOdktQ2r3uTJrotRXGJiIiIghWnrrnQljS8bS3OOLRnKj4o+q7N10DK8Lv3v0BdowXP/etrm7Vf2qRYLBp7DTJT471KE+2LorhEREREwcjjEZ19+/ZhypQpSE9Ph0qlwkcffeSy/Z49e6BSqex+vv76a2/73K7akoa3LaNC2iR5U98oPFysa8KCDcV2CS6+N9di5c5jUEdFePz+DFRRXCIiIqL24PGITk1NDfr27Yv77rsPt99+u+z9vvnmGyQlJVl/v/rqqz09dUjydlRImvrmKnMbkacFbCW+LIpLREREFIw8DnRuueUW3HLLLR6fKC0tDR06dPB4PyWQRoU83Uea+sYVE+RKy1Tlct9nnqQ/Z8pzIiIiCkXtloygf//+0Ol0GDNmDHbv3u2ybV1dHcxms81POJqo1+H/jcgKdDcoRMhNae5JW0+OSURERBRM/B7o6HQ6vPHGG9i4cSM2bdqEa6+9FmPGjMG+ffuc7rNixQpoNBrrT9euXf3dzaDUZBHYXMx1EiSP3JTmnrT15JhEREREwcTvWdeuvfZaXHvttdbfc3JycPr0abzwwgsYMWKEw32WLl2KxYsXW383m81hGewUllfDaOY36uSanAK2rfmiKC4RERFRMAtIHZ0hQ4bg+PHjTl9Xq9VISkqy+QlFruqTyKldwmlD5I7cArat+aIoLhEREVEwC0gdncOHD0OnU3aNDlf1SQDIql3CaUPkjrYNNW+k9Oet34ttOSYRERFRsPA40Pnxxx9x4sQJ6+/l5eUoLi5GcnIyunXrhqVLl6KyshJr164FAKxcuRKZmZno1asX6uvrkZubi40bN2Ljxo2+u4og0mQReGXXCby085jda0ZTLWbnFjncT6pd0rK+zuCsZCQnxKC6pt6vfabQ9MTk63Hv0Kw2jbq0pSguERERUTDzOND5/PPPMWrUKOvv0lqae+65B2+//TYMBgMqKiqsr9fX1+ORRx5BZWUl4uLi0KtXL2zbtg2TJk3yQfeDS16JAU9u+RJGc53D112liXZUuyQyQoXb+qXjrQMn/dBbCmYRKsDBbEYAV9bPtDXIkXiT/pyIiIgo2KmEEEFfpsVsNkOj0cBkMgXteh2pyrwv/pjrZw2xPngWlFVh+psHfXBUCgW39UvHLwd1helSPeauOwzANkCWwpqWI3/tLRTuRyIiIqKAJCNQGldV5r3RMgmBlB2LwsNHxWcwd10RIiJUWDVjALSt/u21mli7IEdOYgsiIiKicBOQZARK467KvKdaJiGQsmPN+WltDx9hle/CpQbMzi3C6zMGYP+S0S7Xz7hKesFkAkRERBTOOKLjA/mlRp8cR4Xmh9TWtUuk7FidktQ+OQ+FhqWbjmLLF2cAALf2SUdOjxS7IGdObpFdkG34KenFX3ce4+gOERERhS2O6LRRXonBq2QBKjheeyHVLmmyCJtv8sdla/G1wYyVn5xwcDRSovOXGrDovWIA9qM0cqZLvrTzONYXnsaTUzm6Q0REROGHyQjaoMkiMOz5XbKnrbmro3PHDd2QmRqPk+dqsL6wwiZ721XqSPxY1+TbC6CQIQXCr97ZHx0T1Dhw4hxe2S0/6H3tzgGY1Mc3wU6w3o9ERERELTHQaQNPMqItGtsT80b3tE49ajli4yiwIXLEVdppd/u9Mr0/JvVJb3MfgvV+JCIiImqJa3TaoGV2NFceGJqJBWOvsVlfIdUuUUdFYOXO4wxySBZvl9xYBPDQusPIKzH4tkNEREREQYqBThu0zI7mythsrcPtvk5LTeTO8q2lTFBAREREYYGBThtINW6c1aZ3lkVN4uu01ETuGEy1KCyvDnQ3iIiIiPyOgU4bSDVuANgFO62zqDkid+obkS/xfUdEREThgIFOG0k1buRUsG9N7tQ3Il/i+46IiIjCAevo+MBEvQ7jsrUuK9g7MjgrGR3ionHhckM79ZRClatsayo0B9aP3XI9Hn7vsNt2zqZSEhERESkJR3R8RMqiNq1fZ7sK9q72uW9opv87RyHvnpwMqOB6iuSt/dLxyvT+DveXM5WSiIiISEnCKtBpsggUlFVhc3ElCsqqgiL71I91jYHuAoWA8b3kTZGc1Ccdr88YAJ0XUymJiIiIlCRspq7llRiwfGupTZYznSYWy6ZkB+zhb/uRM3jzP+UBOTeFhpbTzSIjVLKmSHo7lZKIiIhISVRCiMAPa7jR1krseSUGzMktsqtXIz32BeKb7iaLwA3P5qO6hutzyLFAvj9daev9SERERNQeFD91zVVRTmlbIIooFpZXM8ghq4SYSHSIj7bZxulmRERERN5T/NQ1d0U5Ba4UUczpkdJu/WItE2rpUn0TUN+ERWOvQWZqPKebEREREbWR4gMduQFFewceJ89datfzUXATaJ6qtuG/Fdi/ZLTfApwmi+DaHSIiIgoLig905BZHbM8iik0WgfWFFe12PgoNLUcXB2cl+zwgCcaEHERERET+ovhAZ3BWMnSaWBhNtQ7X6QSiiGJheTWMZk5dI8d2lhqx+J/FNgFJckI0npmmx6Q+6V4d01lCDqOpFnNyi7gWiIiIiBRH8ckIIiNUWDYlG4DrYovtOX2H63PIldUHTtqtK6uuacBD6w5jxfZSj48XrAk5iIiIiPxJ8YEO0FxXRE6xxfaSmqBu1/NRaFABcBdv/31fObYfMXh0XE8SchAREREpheKnrkmCqogi135TKyo0Bxxyqlo9sbkEE/Ra2e/dYE3IQURERORPYRPoAM3T2NozhbQz536sC3QXKMAiVEDLmWJaTSxu0Wvx1oGTbvetqqn3KB16MCbkICIiIvK3sAp0ggUfKMkigNv7p0PXIQ7ATwG4gKxAB/Bs9CUYE3IQERER+RsDHR9zVadEem3Hl56tsSBl2nj4jPX/X9l9AtqkWKijIlDXaHG7ryfBspSQY05ukXWKnCRQCTmIiIiI/I2Bjg+5qlMCwO41opa+NzsecWlN58Xoi5SQo/V7UMs6OkRERKRQDHR8xFWdktm5RQHpE4UWucmd77ihm1ejL0GVkIOIiIjIzxjo+ICcOiVEvtItJd7rfYMlIQcRERGRv4VFHR1/c1enhMiXqpm1j4iIiMgtjuj4AOuPUHtKTojxaj9XiTKIiIiIlIaBjg8wXTS1J60mzuN9XCXKYCICIiIiUiJOXfMBqU4JvxuntpDz/vEm45qUKKP19EqjqRZzcouQV8J050RERKQ8DHR8IDJChal9dUw8QLJ1jI9Gh/hom21aTSx+MyLLacCjguf1buQkyli+tRRNFr57iYiISFk4dc0H8koMeGNfeaC7QUFu7qgeuKZTonV9DADrmpnUq9SAAM7V1GHh2J5YX1gBo/lK0gFvp5m5S5QhABhMtSgsr2Y2NiIiIlIUBjpt5Oobc6KWhv3sartgIqdHCvJKDHjk/S9sC3kmxWLR2GuQmRrfpsQBchNlMKEGERERKQ2nrrURU0uTHCkJMQ7X1uSVGDDbwfqZ7821WLnzGNRREcjpkeJ1djS5iTKYUIOIiIiUhoFOG/GbcJLj6Wl6u2ClySLw6KajDtv7av2Mu0QZKniX4ICIiIgo2DHQaSN+E07u/GZEFib1sV9b88quE7hwqcHpfi3Xz7TWZBEoKKvC5uJKFJRVOQ2GIiNUWDYlG4B9Vjfpd08THBARERGFAq7RaSPpG3OjqZbrdMJY/64anKq+jOqaeuu25IRoPDNNj0l90u3aN1kE1hyQl8Ci9aihpzVxJup1WDVjgN0+WtbRISIiIgVjoNNG0jfmc3KLoAJsgp3Wv5NyHT5twmt39kfHBDXOXqx1m0CgsLwaFy47H81pqeWooVQTp/X7SqqJs2rGAKfBzrhsrTXLW1sSHBARERGFAk5d81LLqUOauBi8emd/aDW209i0mli8ducA6DSc3hYOnt72FQZnJWNav85uEwjklxplHbNDXLR1/Uxba+JERqiQ0yNFVv+IiIiIQh1HdLzgaOqQNkmNO27ohkaLACCQ0z0VQ356mIyIAGbnFgWuw9Qu5NajySsx4K0DJ2Udc8x1V1v/X25NnINlVRjaM1XW8YmIiIiUSiWECPrZVWazGRqNBiaTCUlJSQHti7OpQ621XjOx/cgZzFt/GCxAr2x/vaMfpvXrjCaLcDhNrMkiMPS5XTCa5Wfr6xAfjef+pzfqGi1YsKHYffu4aDx3e2+/rb0JpvuRiIiIyBkGOh5osggMe36XrLo50qSglmsmth8x4KF1HNlRsvWzhsB0ud5psgBNXAymv3nQq2MvGnsNXtp5TFZbFeB0vU5bBcv9SEREROQK1+h4wJPioI7WTEzqo8Nrdw5wWtOEQptOE4vzNfWY46AAqNFUi9m5RXinQF6mNUfWF56CNkkt+/3T1ho8RERERKGMgY4HPC0O6qgOSseEGGZiU6gB3TriqY9dJwvYXvK918c3muswfXA3WW1d1eAhIiIiCgcMdDzgbXHQlgGSp8EShY5tRw0erb3xRmZqAlbNGIAOcdGy2vP9RkREROGKgY4HpOKgnk49axkgeRssEQHN75+Jeh1evWuA7PZERERE4YiBjgek4qAAZAU7KjSv25DqoABXgiUiT2mT1Nb30pDuKS6DbkfvPSIiIqJwwkDHQxP1OqyaMcCuOKgjAsDQHinYUlyJgrIqNFkEIiNUmNrXP2l/SdmenNrLWuTTVdAt/b5sSjaLghIREVHYYnppL7Wsk3Ly3CWsL6xwuz6jQ1w07r0pAxv+expGc1079ZRCwW390lHXaMG+4z+gpq7J5jWpjo6jVNGOite2ruHka8F4PxIRERG15nGgs2/fPvz5z3/GoUOHYDAY8OGHH+K2225zuc/evXuxePFifPnll0hPT8fvf/97zJ49W/Y5A/Fg5azgo7O2B8uqkPvZSfyrDVm1KPyoAGg1sdi/ZLS1oOjBb6tQUFYFQCCneyqG9EhxOTLjyXvVFxjoEBERUSiI8nSHmpoa9O3bF/fddx9uv/12t+3Ly8sxadIkzJo1C7m5uThw4AAeeughXH311bL2DwRPviV31JZILgFgal+dzZS0oT9LxdCfpco+RmSECjk9UvzUQyIiIqLQ1KapayqVyu2IzpIlS7BlyxZ89dVX1m2zZ8/GF198gYKCAlnnac9vkPNKDJiTW2RXC0X6frxltXlnbYk8oYLt+yrYcUSHiIiIQoHfkxEUFBRg/PjxNtsmTJiAzz//HA0NDf4+vUeaLALLt7ou+ChVm3fVtq0iuX487EjvKyIiIiLyDY+nrnnKaDSiU6dONts6deqExsZGnDt3Djqd/bfYdXV1qKu7sljfbDb7vF+O1jUUlle7nILWutq8v6arNfF5N6y0fF9xChoRERGRb/g90AGap7i1JM2Wa71dsmLFCixfvtxv/XG2BmeSXitrf1abJ3/g+4qIiIjId/w+dU2r1cJoNNpsO3v2LKKiopCS4vjb66VLl8JkMll/Tp8+7bP+SOtqWo/GGE21WH3gpKxjpCXGsuI8+RzfU0RERES+4/cRnZycHGzdutVm244dOzBo0CBER0c73EetVkOtVvu8L3LW4ESoACHgsI2UCnhwVjK2FVf6vH8Unlq+r4iIiIjINzwe0fnxxx9RXFyM4uJiAM3po4uLi1FRUQGgeTTm7rvvtrafPXs2Tp06hcWLF+Orr77CW2+9hdWrV+ORRx7xzRV4wN0aHACw/BTkuKo2/6e8r/DwP7+Qdc6+XZiVipxr+b7yZ+0bIiIionDjcaDz+eefo3///ujfvz8AYPHixejfvz/+93//FwBgMBisQQ8AZGVlYfv27dizZw/69euHp59+Gi+//HJAaujIXQNx/9BMaDW204i0mlismjEAFgvw933lss95701Z0Cb5fnSKlEF6X4VKamkiIiKiUNGmOjrtxVd1OwrKqjD9zYNu262fNcSaha1lVjYAuOHZnaiuqZd9zvWzhsB0uR6zc4u87jcp0xOTr8e9Q7NCbiSHdXSIiIgoFLRL1rVgMTgrGTpNLIymWrdrcKRq81Ia6o+PnMG5i3UeBTm6FsdaNLYnXtp53GfXQqFLep+FYpBDREREFCrCKtCJjFBh2ZRszMktggq2CQccrZVwlIbaE7fotSgsr8bAjI4YlJmM+JhIXKpvatM1kDJwTQ4RERGRf4XV1DWJszo6y6ZkW9dKSGmoffHHiVA1JzkgAoDfjMjC0knZge6G1zh1jYiIiEJBWI3oSCbqdRiXrbVbgyN9w+4qDbU3GOSQRAVgyxcG/H7i9RzRISIiIvKjsAx0AFjX4DgiJw01kTcEAIOpFoXl1U7ff0RERETUdh6nlw4HctNQt5QUG7YxI3nBaLoc6C4QERERKRqfzh1IS4x13wjN6YGTE2JQXVOPk1U1eOdghfudiAA8ve0rxMVEsn4OERERkZ8w0HFAbhpqnSYWT2/7itPcwlR8dAQuNVi82vd8TT3m5BaxWCgRERGRn3DqmgNSGmrgStppifT71L46zF13mEFOGPM2yAGupDZfvrUUTcxWQURERORzigl0miwCBWVV2FxciYKyqjY/PE7U67BqxgBoNbbT2LSaWLx65wBs+cLgs6xsFJ5aJiYgIiIiIt9SxNQ1OXVxvOEsDTWzspEveZP8goiIiIhcC/kRHamwZ+vAw2iqxZzcIuSVGNp0fCkN9bR+nZHTIwWRESqPHkyHZCXbTX8jaklu8gsiIiIiki+kAx1XhT39uQbCkwfTg+XVeHB4pk/PT8qgQvPI4+Cs5EB3hYiIiEhxQjrQcTeFzF9rIAZnJSM5IVp2+/cPVUITp4hZguQj0ijfsinZiIzgmB8RERGRr4V0oCN3Cpmv10BERqjwzDS97PYXLjXAdLnRp32g0NAhLhoLx/SENsk+qQVTSxMRERH5T0gPM8idQuaPNRCT+qTjN99dwN/3lfv82BScIlSA3FmQ0hjNc7f3xkS9DvPH9LRLasGRHCIiIiL/CelAR25hT3+tgVg6KRtx0VFY+clxvxyfgsvo69LwyVdnAcBtanFtq6x/UlILIiIiImofIR3oSIU95+QWQQXbh8/2WgMxf0xPbPhvBYzmOr+dg4LDzq/O4jcjsrDlC4NdKvMnJl+PjglqjtgQERERBQmVECLo616azWZoNBqYTCYkJSXZve6vOjpySSmuAfff9FNo6xgfjc/+MBaHTp2H0XQZ1TX1SL5KDW1S+AQ37u5HIiIiomCgiEAHaE41Hcg1EI6CLW2SGrWNFly41NBu/SD/e/fBG3GxtiGgwXUgMdAhIiKiUKCYQCcYOAq2Xtl1Ai/tPBborpEP3aLXIq/EaDd6J4XVSs+mFir3IxEREYW3kF6jE2wcLTjvlhwXoN6Qv+w79oPTIrUqNBepHZetDYtpbERERETBioGOD7Uc0UlNUAMqYH1hRaC7RT5WU9/k9LWWRWqZZY2IiIgocBjo+IijNTqkPPExkbjkItCR7Cw1MtAhIiIiCqCIQHdACaSsawxylK9v5w6y2q0+cBJ5JQb/doaIiIiInGKg00KTRaCgrAqbiytRUFaFJov7PA1NFoHlW0uZVjpMlBpN0CbFQs7qm+VbS2W9h4iIiIjI9zh17See1OJpuRbn3MU6juSEEdPlRtw/tLusTHpcq0NEREQUOAx0cGXqWevv3o2mWszJLbJJF8y1OMqlgryCr5mp8bh/aCbeOnDSbduzF/k+ISIiIgqEsJ+65mrqmbRNmoLEtTjK1CEuGu8+eCMWjPmZrPapCWqMy9bKapuWGNuWrhERERGRl8I+0Cksr3YZuEjpgg+WVXEtjkJduNyACJUK88dcgw7x0W7b//b9L3C+ph46jfO1Oio0T30cnJXs074SERERkTxhH+jInVpU8O05juQo2NmLtYiMUOHXg7q4bfu9uRZz1xVhat/m6Yytgx3p92VTslk0lIiIiChAwj7QkT+1iA+sSpaWGIu8EgPe2Ffutq00qrflCwNevbM/tBrb91ByQgzuG5oJTVwMs64RERERBUjYJyMYnJUMnSYWRlOtw2lpKgBaTSxyeqTgld0n3B7vicnXIzVRjdQENaACCsqqZO1HgaNNUmNgRkeM/PNu2VMTpSmNHRPU2L9kNGEDGjQAACA7SURBVArLq7Gz1IgPiytRVVOPtw6cxFsHTjrN3EdERERE/hX2IzqRESosm5INwPUUpCHdU2Stybh3aBam9euMoT1TMfRnqVg07hroNFyQHswuNzRh1Z4yr6YmSlPeTJebg5vqmgab16XMfSweSkRERNS+wj7QAYCJeh1WzRhgNwVJq4m1ppaWGxC1XpPRcj8KTqbLjbLq4jiSlhjrUeY+IiIiImofYT91TTJRr8O4bC0OfluFgrIqAAI53VMxpEWxRykgal1HR+tmetJEvQ6zhmfhzf+4X/9BoUPKqiY3cx+LhxIRERG1HwY6LeSXGm2CmFd2l9mtsZACosLyapy9WIu0xOaHXVfZtZosAh8f4dQlpbnc0IT8UiPqGi2y2rN4KBEREVH74dS1nzgrBupojUVkhAo5PVIwrV9n5PRIcRvkvH2gnKmpFch0qQFzcotw8twlWe1ZPJSIiIio/TDQAfy2xiKvxIBhz+/C09u+anMfqX0sHPMzyC19I70bNvy3AtokNYuHEhEREQURBjqAR2ss5HI2QkTBSQpG5o+5Bq9MHyB7P+m9MX1wN+txWh8XYPFQIiIiovbGQAfy107IbedqhIiCT+tgZFIfHV6fMcCjtOCZqQluM/cRERERUfthMgLIXzsht527ESIKnPuGZiKvxOg2a56UdOLtA+Wyph6mJTYXlfU0UQURERER+QcDHQCDs5Kh08TCaKp1OAqjQvPDsNw1Fm3NrvXQzd0RoYrAK7tPtOk4ZG9zcSWemaZHxwS122AkMkKFe4dm4f/2l8t+b0iJKoiIiIgosDh1DfC6GKgzbcmupdPEYuHYa9ExPtrrY5Bz1TUNmLvuMEyX62VlzfP1e4OIiIiI2gcDnZ9IxUB9scZCGiHyhr5zEkb+eTcztfnZk1u+xIET57C5uBIFZVUuM+r58r1BRERERO1DJYQI+jXzZrMZGo0GJpMJSUlJfj1Xk0X4ZI3Fiu2l+Pu+cj/0kPyhdWFYR3z13gh17Xk/EhEREXmLgY4fNFkEhj2/iwkJQogUrnCExr1Qux+JiIgoPHHqmh8w61roaUthWCIiIiIKPgx0/KCtWdcoMLwpDEtEREREwYmBjh+0JesaBR4DVSIiIqLQxzo6fjA4Kxkd4qJx4XJDoLtCXvAmUGWiAiIiIqLgwkDHDyIjVLhvaCZe2nk80F0hD3haGFaSV2LA8q2lNuuy5GRxIyIiIiL/4dQ1P5k3uic6sOhnSBEA7rihKz4+csZtbR1JXokBc3KL7JJPGE21mJNbhLwSg596S0RERESucETHTyIjVHjuf3pjTm4RmMMr+MXHRCImKsJmFM7dqEyTRWD51lKH/74CzSNEy7eWYly2ltPYiIiIiNqZVyM6r732GrKyshAbG4uBAwfiP//5j9O2e/bsgUqlsvv5+uuvve50qJio12HVjAHQJjE5ga/E+GkM8lJ9Ey5csl1T5W5Uxl0acWdZ3JosAgVlVdhcXCl75IiIiIiIPOPxiM57772HhQsX4rXXXsPQoUPx97//HbfccgtKS0vRrVs3p/t98803NsUFr776au963E58tbh8ol6Hcdla/O2TY1j5yQk/9DR0jbnuanzy9Q8e7dMggHHZaSipNLutVST9a2nio2G61OB0ZC1CBTiKNdyNysjNztayHdfzEBEREbUPjwOdF198EQ888AAefPBBAMDKlSvx73//G6tWrcKKFSuc7peWloYOHTp43dH25OuH0fxSI977/DuHr3WIi8bdORlYW1COC5ebvO5zKElJiMHT0/TomBDjcaAjBJBfehazhmdh9HWdrIHo+Zp6PL3N9t9M+9O/GQDMyS2CCrAJdqTfXQ2otByVyemRYvOa3OxsUjtpPU/r00kjR6tmDGCwQ0REROQjHgU69fX1OHToEB599FGb7ePHj8enn37qct/+/fujtrYW2dnZePzxxzFq1CjPe9sOfP0w6ux4kguXG/DyrvAY6blvaCbGZ2uto2NNFgGdJtbtyIwjq/eX43cTrkNM1JW5bBP0WqejcKtmDLALXrWaWEzSa7H6wEm353M0ejM4Kxk6TSyMplqH/74ts7hxPQ8RERFR+/JoxcO5c+fQ1NSETp062Wzv1KkTjEajw310Oh3eeOMNbNy4EZs2bcK1116LMWPGYN++fU7PU1dXB7PZbPPTHtw9jALND6Ny11S4Op5cHRWUuS2vxGgTfERGqDC1r3cjGBYBvFNw0mZbZIQKOT1SMK1fZ+T0SLEJGCbqddi/ZDTWzxqCv97RD+tnDcH+JaMxNlsr63yORm8iI1TWEaPWoYn0+7Ip2YiMUHm9noeIiIiIvOPV0m6VyvaxTghht01y7bXXYtasWRgwYABycnLw2muvYfLkyXjhhRecHn/FihXQaDTWn65du3rTTY/5+mHU3fHcSUmIwWOTrvd6/2DT+m/XZBHY8oX36ZdPVV/yqL2jQEgalXE2hqJC87RFZ7V1rAknNLaBkFYTazP65816HiIiIiLynkdT11JTUxEZGWk3enP27Fm7UR5XhgwZgtzcXKevL126FIsXL7b+bjab2yXY8fXDaFsfWqtq6vHk1i/bdIxgYzRf+Zu0NRA0X25AQVmV14kigCujMs7W8ABXRmWckRJOuEpe4el6HiIiIiJqG49GdGJiYjBw4EDk5+fbbM/Pz8dNN90k+ziHDx+GTud8ypJarUZSUpLNT3vw9cOoLx5af6xTVoKC6h/rrP/f1kDwo+IzmP7mQQx7flebCnPKHZVxxdW0OQBtHjkiIiIiIs94nHVt8eLFmDlzJgYNGoScnBy88cYbqKiowOzZswE0j8ZUVlZi7dq1AJqzsmVmZqJXr16or69Hbm4uNm7ciI0bN/r2SnzAk8XlvjheOOoQd2XNka9GL3yRtUzOqExb+GLkiIiIiIjk8zjQ+fWvf42qqio89dRTMBgM0Ov12L59OzIyMgAABoMBFRUV1vb19fV45JFHUFlZibi4OPTq1Qvbtm3DpEmTfHcVPuLrh1FXxwtXT35ciriYSEzqk+6zQNBXWcukURl/kUaOHGV/Yx0dIiIiIt9SCSGC/vnbbDZDo9HAZDK1yzQ2X9fRcXS8cPebEVlYOinbmn4b8E0guH7WEL8GK77gq2K0gdLe9yMRERGRNxjoOOHrh9Emi8Aru07gzf+UKW7djbdeu3MAJvXROQwEI1SuC3k689c7+mFav84+7CW1xkCHiIiIQoHHU9fCha+nMeWXGrFy5zFOX2vhic0lmKDXOlwfU3WxFvM2FHt8zNSr1L7vKBERERGFHK/q6ISLJotAQVkVNhdXoqCsymWhUFdtfVE4tL1dpY7CS7/qi0Vjr3GaKaytqmrqrXV1WmYtM12ux7P/+tqrY/72n8VtysBGRERERMrAER0nPFmn465tW+vFBMILv+xjvc5rtVfhDx8eRXVNg8/P0zrFtLRmx9ug8HtzXZszsBERERFR6OOIjgPSw3br4ERKY9xyxEBO22Cudp+cEG3zu04Ti9dbBQkT9TocXDoWibG+j4tbppj2xciX+Oln+dZSlyNwRERERKRsHNFpxdXDdus0xvjp/921feGXff3W37aIUAEHloxB8ekLbpMuxERF4Pn/6YOH1hX59PwDMzpaf/flyJfBVIvC8uqgz8BGRERERP7BQKcVdw/bAlceovHT/7trC4GgLBxqEUDx6QtOg4HWmecm6LX4zYgs/H1fuc/Of+jUeev5fT3ylV9qZKBDREREFKYY6LQi92Hbk4fyczV1QVs41Gh2fB2u1h29ckcHzN9w2CfX0fLv2HIamy9sLj6DxybLL/BKRERERMrBNTqtyH3YTkuMld32+Pc/QhMXg1fvHACtxrcP821V/WOd3TZ3646iolR49c7+Pjl/y7/h4Kxk6DSxPsvy1jKrGxERERGFF47otCI9bDubZqYCoNU0r2UB5E1Je2X3Cbyy+wR0mlg8Mfl6dExQW6eD7fzKiNX7T/rhSuRJToix+V3uGqX9S0Zj0dgf8dLO416dt/XfEWhOMe3rka9gTgRBRERERP7DEZ1WpIdtAHYjC9Lvy6Y0T4dy1dYRo6kWc9cdhulyPab164zzNXVYc+Ckr7ruFa0mzuZ3T9YozRvdEx3jo522dab137GliXodVs3w3ciXr6fDEREREVFoYKDjgLOHba0m1q4+iycP5tIIxfKtpdh+5AweWncY/siArPrpp4ObIEQTFwWLRdikYfZkjVJkhArP3qZ327b1EhlHf8eWJup1P40YXYMOcY6vQaeJRYf4aKcBpuqnNi1HjIiIiIgofHDqmhMT9TqMy9baZB1zlnq5ZdsDJ37AK7vLnB5XGhF5fHOJ3/qu/SlpAACXxTdNlxtx1+rPbIqberJGCQAm9UnHb7674DQTmwrAK9MHoGNCjNu/Y0v5pUas3HnMad8n9OqEjvFqrNx5zG6am6sRIyIiIiIKDwx0XIiMUMlOTyy1lTsiUl3T0Jau2dAmqfGXX/bDuZo6u0Di1Tv74/cbj+DHuian+xt+SjKwasYAjMvWul131HqkZOmkbPTt0hGPby5BdU29dXtKQgym9tXBYLqMuiYLtEnyghw5hUPf/vQUgCujVhcuXfl7alsEbkREREQUnhjo+Fgg1oRMH9wNQ3um2m3PKzHgqY9LXQY5EgHg9x8cwWd/SHObEOByQxPyS402gcSkPjpM0DePau0sNeLD4kpU1dRjzU8BiUQnIwjxpHCo6VIDBIBFY3siMzVB9ohR6xpBcvYhIiIiotDBNTo+5i5Fsgr2mc7aqqFJYHNxJQrKqqzrbaQU0UazffpoZ8y1jchelofDFeexasYAaJys8TFdasCc3CLklRhstkdGqGC6XI+3Dpx0OmIljR613rclTzKlSZngNvz3NG7tk46cHiluA5a8EgOGPb8L0988iAUbijH9zYMY9vwul30iIiIiotDCQMfH5GRtu3tIhqxj3T6gs6x2r+w+YfPAvv3IGbdTv5wRAvj7vnIcOnUesVGO3x4tkyq0TGQgZ8qZtH/rfVvydFSsZSY4d9zVCGKwQ0RERKQMDHT8wF3WtqyrE2QdJzE2yi5jmTtGUy0eWndY9tQvZ/5vf7nL0SBHwcXBsirZ53UVmHhbONTdSJC7GkGA6wCMiIiIiEIH1+j4iausbQVlVbKO8Xar9S1y+OoRXcg8kBRc5JUY8OjGox6dw1lg4m3hUHcjQZ7UCJKbhIKIiIiIghMDHT9ylrVNGrFwldksQgW/1NjxtbTEWOt0ME+76yowkUbFlm8tdTtKpELzaJm7mjme1AgiIiIiotDGqWsBIGcdTzAEOYmxUW4Lcg7M6OjVeqDkhGi3gYlUOHT9rCF4YGim034A8mrmeFojiIiIiIhCFwOdAHG1jmfMdVf77DzeJkyOUAErbuvt8Bgtg4tDp857tR7omWl6WemcpVGxJ6b0wuszBkDnZN2TnJo5cjLita4RREREREShiVPXAsjROp6BGR0xZMXONh1Xmsr1xORsPL3N/dQvRx4YloVb+6UjKkplN32sZUHOzcWVHh/7NyOyMKlPusf7uVr3JIertT+ejAwRERERUfALy0AnmIpFtl7HU1BW5bQGTUtXqaPwY12jywf2iforRTz/XWLA2wXykxuMvq4TAPfBhSfTvFISYvD0ND0m9XE/+uKMs3VPcjlb+6OVUciUiIiIiEJH2AU6eSUGu4dcXRA95MpdCP+rQV0wOCvZ7QO7FBjk9EhBpeky8kvPetwPV8HF4KxkaJPUbguTJifEoGDpGMQ4qc3Tnto6MkREREREwS+sAh1n2cGkYpFy13r4k9wRknHZWuT0SPHogf3Nu2/AQ7n/xfYS98FO6lVqWf2IjFBh+uBueGnncZftqmvqcejU+aBJ29zWkSEiIiIiCm6B/3q9nYRKsUg5xTJbLpiXHtin9euMnB4pbkcl7roxS15HPPgzZKbKK4DKtM1ERERE1F7CJtDxpFhkILlLPa1C2xbMn6txPcXM03aA/FGok+dqZB+TiIiIiKgtwibQCaVika5ST7d1ep0/aslIo1DuvLTzOPJKDLKPS0RERETkrbBZoxNqxSL9tWBeCkqMplqHs9Ok1NSe1JKJjFBhal8d/r6v3GU7FZqnB47L1nLhPxERERH5VdiM6IRisUhP19/IPaarqXGA51PjmiwCW75wP1ITLNMDiYiIiEj5wibQ8ccDfqjy9dQ4d+ufWguG6YFEREREpGxhM3UNYLHIlnw5Nc7TwCVYpgcSERERkXKFVaADsFhkS76qJeNJ4BJs0wOJiIiISJnCLtABgrNYZJNFyAq+5LZrT+4SHEjamhqbiIiIiEiusAx0gk1eicFuOp3OwXQ6ue2c8VeQJK1/mpNbBBUc1xrtGB+NFf/TO6ymBxIRERFR4KiEEK6+hA8KZrMZGo0GJpMJSUlJge6OT+WVGDAnt8guOJDCDyk5gNx2rs7TliBJDkfn6BAXjfuGZmLe6J4cyVEIJd+PREREpBwMdAKoySIw7PldTjOWSTVt9v5uFEb+ebfbdvuXjHYYTLQ1SPJEME6tI99S6v1IREREysKpawHkLi2zVHfmnYKTstoVllfbrT1qsggs31rqcDqZgO+LeAbj+iciIiIiCj9hU0cnGMlNy3yq+pLXx5MbTLGIJxEREREpCQOdAJKbljkjOd7r48kNpljEk4iIiIiUhIFOAElpmZ1NGFOhOWHAzJxMWe0c1aeRG0yxiCcRERERKQkDnQCS0jIDcBjECABPTL4eMVERTttJvzurTyM3mGIRTyIiIiJSEgY6ATZRr8OqGQOg1TgeUXl621fIKzE4bafVxLrMmuYqmHIXJBERERERhSqmlw4S248Y8NC6IrvtrVNAe5u+uT3q6FB4CIf7kYiIiEIfA50gILeejrM6OZ6chzVuqK2Ufj8SERGRMrCOThDwJAV0W2rUsMYNEREREYULrtEJAkwBTURERETkWwx0ggBTQBMRERER+RanrgUBKQW00VQLRwumpDU6oZICmmuBiIiIiCjQGOgEASkF9JzcIqgAm2An1FJAM7sbEREREQUDTl0LAk0WAU1cDO4fmomOCdE2r7mrkxNM8koMmJNbZJdYwWiqxZzcIuSVGALUMyIiIiIKNxzRCTBHIyDJCTG4rV86xmVrQ2baV5NFYPnWUodT7wSaR6aWby3FuGxtSFwPEREREYU2jugEkLMRkPM19Vhz4CRMl+tDJijwJEU2EREREZG/eRXovPbaa8jKykJsbCwGDhyI//znPy7b7927FwMHDkRsbCy6d++O119/3avOKom7ERCgeQSkyRL09VwBMEU2EREREQUXjwOd9957DwsXLsRjjz2Gw4cPY/jw4bjllltQUVHhsH15eTkmTZqE4cOH4/Dhw/jDH/6Ahx9+GBs3bmxz50OZ0kZAmCKbiIiIiIKJx4HOiy++iAceeAAPPvggrr/+eqxcuRJdu3bFqlWrHLZ//fXX0a1bN6xcuRLXX389HnzwQdx///144YUX2tz5UKa0ERApRbaziXYqNGdfC5UU2UREREQU2jwKdOrr63Ho0CGMHz/eZvv48ePx6aefOtynoKDArv2ECRPw+eefo6GhweE+dXV1MJvNNj9Ko7QREClFNgC7YCfUUmQTERERUejzKNA5d+4cmpqa0KlTJ5vtnTp1gtFodLiP0Wh02L6xsRHnzp1zuM+KFSug0WisP127dvWkmyFBiSMgE/U6rJoxAFqNbXAWSimyiYiIiEgZvEovrVLZPp4LIey2uWvvaLtk6dKlWLx4sfV3s9msuGBHSUVCW5qo12FcthaF5dU4e7EWaYmxIZMim4iIiIiUw6NAJzU1FZGRkXajN2fPnrUbtZFotVqH7aOiopCSkuJwH7VaDbVa7UnXQpI0AtK6jo5WE4tlU7JDdgQkMkKFnB6O/22JiIiIiNqDR4FOTEwMBg4ciPz8fPz85z+3bs/Pz8e0adMc7pOTk4OtW7fabNuxYwcGDRqE6OhoL7qsLBwBISIiIiLyPY+nri1evBgzZ87EoEGDkJOTgzfeeAMVFRWYPXs2gOZpZ5WVlVi7di0AYPbs2XjllVewePFizJo1CwUFBVi9ejXWr1/v2ysJYRwBISIiIiLyLY8DnV//+teoqqrCU089BYPBAL1ej+3btyMjIwMAYDAYbGrqZGVlYfv27Vi0aBFeffVVpKen4+WXX8btt9/uu6sgIiIiIiJqQSWkzABBzGw2Q6PRwGQyISkpKdDdIQprvB+JiIgoFHhcMJSIiIiIiCjYMdAhIiIiIiLFYaBDRERERESKw0CHiIiIiIgUh4EOEREREREpDgMdIiIiIiJSHI/r6ASClAHbbDYHuCdEJN2HIZCZnoiIiMJYSAQ6Fy9eBAB07do1wD0hIsnFixeh0WgC3Q0iIiIih0KiYKjFYsGZM2eQmJgIlUrltJ3ZbEbXrl1x+vRpxRcyDKdrBXi9wUQIgYsXLyI9PR0REZz9SkRERMEpJEZ0IiIi0KVLF9ntk5KSgu7h0F/C6VoBXm+w4EgOERERBTt+HUtERERERIrDQIeIiIiIiBRHUYGOWq3GsmXLoFarA90VvwunawV4vURERETkmZBIRkBEREREROQJRY3oEBERERERAQx0iIiIiIhIgRjoEBERERGR4jDQISIiIiIixVFMoPPaa68hKysLsbGxGDhwIP7zn/8Euks2VqxYgRtuuAGJiYlIS0vDbbfdhm+++camjRACTz75JNLT0xEXF4ebb74ZX375pU2buro6zJ8/H6mpqUhISMDUqVPx3Xff2bQ5f/48Zs6cCY1GA41Gg5kzZ+LChQs2bSoqKjBlyhQkJCQgNTUVDz/8MOrr6/1y7UDz9atUKixcuNC6TWnXW1lZiRkzZiAlJQXx8fHo168fDh06pNjrJSIiIgpqQgE2bNggoqOjxZtvvilKS0vFggULREJCgjh16lSgu2Y1YcIEsWbNGlFSUiKKi4vF5MmTRbdu3cSPP/5obfPcc8+JxMREsXHjRnH06FHx61//Wuh0OmE2m61tZs+eLTp37izy8/NFUVGRGDVqlOjbt69obGy0tpk4caLQ6/Xi008/FZ9++qnQ6/Xi1ltvtb7e2Ngo9Hq9GDVqlCgqKhL5+fkiPT1dzJs3zy/XXlhYKDIzM0WfPn3EggULFHm91dXVIiMjQ9x7773is88+E+Xl5WLnzp3ixIkTirxeIiIiomCniEBn8ODBYvbs2TbbrrvuOvHoo48GqEfunT17VgAQe/fuFUIIYbFYhFarFc8995y1TW1trdBoNOL1118XQghx4cIFER0dLTZs2GBtU1lZKSIiIkReXp4QQojS0lIBQBw8eNDapqCgQAAQX3/9tRBCiO3bt4uIiAhRWVlpbbN+/XqhVquFyWTy6XVevHhR9OzZU+Tn54uRI0daAx2lXe+SJUvEsGHDnL6utOslIiIiCnYhP3Wtvr4ehw4dwvjx4222jx8/Hp9++mmAeuWeyWQCACQnJwMAysvLYTQaba5DrVZj5MiR1us4dOgQGhoabNqkp6dDr9db2xQUFECj0eDGG2+0thkyZAg0Go1NG71ej/T0dGubCRMmoK6uzmaqlS/MnTsXkydPxtixY222K+16t2zZgkGDBuGXv/wl0tLS0L9/f7z55puKvV4iIiKiYBfygc65c+fQ1NSETp062Wzv1KkTjEZjgHrlmhACixcvxrBhw6DX6wHA2ldX12E0GhETE4OOHTu6bJOWlmZ3zrS0NJs2rc/TsWNHxMTE+PRvtmHDBhQVFWHFihV2rynter/99lusWrUKPXv2xL///W/Mnj0bDz/8MNauXWvtg9R3V9cSKtdLREREFOyiAt0BX1GpVDa/CyHstgWLefPm4ciRI9i/f7/da95cR+s2jtp706YtTp8+jQULFmDHjh2IjY112k4p12uxWDBo0CD88Y9/BAD0798fX375JVatWoW7777baT9C9XqJiIiIgl3Ij+ikpqYiMjLS7pvqs2fP2n2rHQzmz5+PLVu2YPfu3ejSpYt1u1arBQCX16HValFfX4/z58+7bPP999/bnfeHH36wadP6POfPn0dDQ4PP/maHDh3C2bNnMXDgQERFRSEqKgp79+7Fyy+/jKioKOt5lHK9Op0O2dnZNtuuv/56VFRUWPsAKOd6iYiIiIJdyAc6MTExGDhwIPLz82225+fn46abbgpQr+wJITBv3jxs2rQJu3btQlZWls3rWVlZ0Gq1NtdRX1+PvXv3Wq9j4MCBiI6OtmljMBhQUlJibZOTkwOTyYTCwkJrm88++wwmk8mmTUlJCQwGg7XNjh07oFarMXDgQJ9c75gxY3D06FEUFxdbfwYNGoS77roLxcXF6N69u6Kud+jQoXbpwo8dO4aMjAwAyvv3JSIiIgp6AUiA4HNSeunVq1eL0tJSsXDhQpGQkCBOnjwZ6K5ZzZkzR2g0GrFnzx5hMBisP5cuXbK2ee6554RGoxGbNm0SR48eFdOnT3eYfrhLly5i586doqioSIwePdph+uE+ffqIgoICUVBQIHr37u0w/fCYMWNEUVGR2Llzp+jSpYvf0w+3zLqmtOstLCwUUVFR4tlnnxXHjx8X7777roiPjxe5ubmKvF4iIiKiYKeIQEcIIV599VWRkZEhYmJixIABA6xpm4MFAIc/a9assbaxWCxi2bJlQqvVCrVaLUaMGCGOHj1qc5zLly+LefPmieTkZBEXFyduvfVWUVFRYdOmqqpK3HXXXSIxMVEkJiaKu+66S5w/f96mzalTp8TkyZNFXFycSE5OFvPmzRO1tbX+unwhhH2go7Tr3bp1q9Dr9UKtVovrrrtOvPHGGzavK+16iYiIiIKZSgghAjmiRERERERE5Gshv0aHiIiIiIioNQY6RERERESkOAx0iIiIiIhIcRjoEBERERGR4jDQISIiIiIixWGgQ0REREREisNAh4iIiIiIFIeBDhERERERKQ4DHSIiIiIiUhwGOkREREREpDgMdIiIiIiISHEY6BARERERkeL8f8L5gZjSCTA6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig1 = plt.subplot(2,3,1)\n",
    "plt.scatter(data.loc[:,'Avg. Area Income'], data.loc[:,'Price'])\n",
    "plt.title('Price VS Income')\n",
    "\n",
    "fig2 = plt.subplot(2,3,2)\n",
    "plt.scatter(data.loc[:,'Avg. Area House Age'], data.loc[:,'Price'])\n",
    "plt.title('Price VS House Age')\n",
    "\n",
    "fig3 = plt.subplot(2,3,3)\n",
    "plt.scatter(data.loc[:,'Avg. Area Number of Rooms'], data.loc[:,'Price'])\n",
    "plt.title('Price VS Number of Rooms')\n",
    "\n",
    "fig4 = plt.subplot(2,3,4)\n",
    "plt.scatter(data.loc[:,'Area Population'], data.loc[:,'Price'])\n",
    "plt.title('Price VS Area Population')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7b53530b-e75e-4a6c-bc7b-cfbe947ff8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23086.80050\n",
       "1    40173.07217\n",
       "2    36882.15940\n",
       "3    34310.24283\n",
       "4    26354.10947\n",
       "Name: Area Population, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X =  data.loc[:,'Area Population']\n",
    "y = data.loc[:,'Price']\n",
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5eafdf69-2242-4281-8bd1-1d83498c2ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.059034e+06\n",
       "1    1.505891e+06\n",
       "2    1.058988e+06\n",
       "3    1.260617e+06\n",
       "4    6.309435e+05\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9e6054eb-5b89-4e9c-8860-0f967c3b2799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up the linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "LR1 = LinearRegression()\n",
    "\n",
    "X = np.array(X).reshape(-1,1)\n",
    "print(X.shape)\n",
    "\n",
    "# train the model\n",
    "LR1.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e6835d69-66b3-4755-bdcc-5e4f44f26903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1042003.96655067 1290351.13160376 1242518.05996289 ... 1189959.67738035\n",
      " 1325998.65917659 1382331.02332736]\n"
     ]
    }
   ],
   "source": [
    "# calculate the price vs area population\n",
    "y_predict_1 = LR1.predict(X)\n",
    "print(y_predict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0a7e5748-04a3-4e44-b2d6-4d7a0a5f3f07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103857945143.86589 0.16691790652769944\n"
     ]
    }
   ],
   "source": [
    "#evaluate the model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "mead_squared_error_1 = mean_squared_error(y, y_predict_1)\n",
    "r2_score_1 = r2_score(y, y_predict_1)\n",
    "print(mead_squared_error_1,r2_score_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0bbcf2dc-cbc8-4c97-88b3-ae2db6fd7553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.figure()\n",
    "plt.scatter(X,y)\n",
    "plt.plot(X,y_predict_1, 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "994d8fe4-5eb2-4e20-95fe-61aeb94db563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Avg. Area Number of Bedrooms</th>\n",
       "      <th>Area Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.682861</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>4.09</td>\n",
       "      <td>23086.80050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>6.002900</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>3.09</td>\n",
       "      <td>40173.07217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.865890</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>5.13</td>\n",
       "      <td>36882.15940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>7.188236</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>3.26</td>\n",
       "      <td>34310.24283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.040555</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>4.23</td>\n",
       "      <td>26354.10947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>60567.94414</td>\n",
       "      <td>7.830362</td>\n",
       "      <td>6.137356</td>\n",
       "      <td>3.46</td>\n",
       "      <td>22837.36103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>78491.27543</td>\n",
       "      <td>6.999135</td>\n",
       "      <td>6.576763</td>\n",
       "      <td>4.02</td>\n",
       "      <td>25616.11549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>63390.68689</td>\n",
       "      <td>7.250591</td>\n",
       "      <td>4.805081</td>\n",
       "      <td>2.13</td>\n",
       "      <td>33266.14549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>68001.33124</td>\n",
       "      <td>5.534388</td>\n",
       "      <td>7.130144</td>\n",
       "      <td>5.44</td>\n",
       "      <td>42625.62016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>65510.58180</td>\n",
       "      <td>5.992305</td>\n",
       "      <td>6.792336</td>\n",
       "      <td>4.07</td>\n",
       "      <td>46501.28380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0          79545.45857             5.682861                   7.009188   \n",
       "1          79248.64245             6.002900                   6.730821   \n",
       "2          61287.06718             5.865890                   8.512727   \n",
       "3          63345.24005             7.188236                   5.586729   \n",
       "4          59982.19723             5.040555                   7.839388   \n",
       "...                ...                  ...                        ...   \n",
       "4995       60567.94414             7.830362                   6.137356   \n",
       "4996       78491.27543             6.999135                   6.576763   \n",
       "4997       63390.68689             7.250591                   4.805081   \n",
       "4998       68001.33124             5.534388                   7.130144   \n",
       "4999       65510.58180             5.992305                   6.792336   \n",
       "\n",
       "      Avg. Area Number of Bedrooms  Area Population  \n",
       "0                             4.09      23086.80050  \n",
       "1                             3.09      40173.07217  \n",
       "2                             5.13      36882.15940  \n",
       "3                             3.26      34310.24283  \n",
       "4                             4.23      26354.10947  \n",
       "...                            ...              ...  \n",
       "4995                          3.46      22837.36103  \n",
       "4996                          4.02      25616.11549  \n",
       "4997                          2.13      33266.14549  \n",
       "4998                          5.44      42625.62016  \n",
       "4999                          4.07      46501.28380  \n",
       "\n",
       "[5000 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define X_multi\n",
    "X_multi = data.drop(['Price','Address'],axis=1)\n",
    "X_multi.head()\n",
    "X_multi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "47882626-a3ff-4046-a14d-510f48a102db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR_multi = LinearRegression()\n",
    "#train model\n",
    "LR_multi.fit(X_multi, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "277202a8-7b9f-4fa4-9c6d-23852bdb24a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1223847.04271238 1494937.69140469 1253016.74601791 ... 1020482.52620012\n",
      " 1263982.82466458 1301976.3454874 ]\n"
     ]
    }
   ],
   "source": [
    "y_predict_multi = LR_multi.predict(X_multi)\n",
    "print(y_predict_multi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c930bf94-07a7-4382-877c-be3499b80044",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10219734313.031612 0.9180238195119546\n"
     ]
    }
   ],
   "source": [
    "mead_squared_error_multi = mean_squared_error(y, y_predict_multi)\n",
    "r2_score_multi  = r2_score(y, y_predict_multi)\n",
    "print(mead_squared_error_multi,r2_score_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0e348a39-51e3-4a19-a414-465818952b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_multi = plt.figure()\n",
    "plt.scatter(y,y_predict_multi)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fb894257-0ebc-46c4-98de-c1d48b7cdb00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8 = plt.figure()\n",
    "plt.scatter(y,y_predict_1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2302e138-9f41-4ff0-892f-bb1007067c9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[41545     5     6     4 70000]]\n",
      "get [881962.48947987]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cc/miniconda3/envs/TF_GPU/lib/python3.10/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "XXX = [41545,5,6,4,70000]\n",
    "XXX = np.array(XXX)\n",
    "XXX = XXX.reshape(1,-1)\n",
    "\n",
    "print(XXX)\n",
    "yyy = LR_multi.predict(XXX)\n",
    "print('get',yyy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7f6eebc-b6a4-4fff-a906-df40f7895ac4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db275224-fd36-4d6f-ad11-d0e6b1d1dc7b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe3ad2d5-e0fa-45de-9a72-fa7ab01bd302",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6266f5b5-0b87-4b26-8b50-b4ed47a1233f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cc/miniconda3/envs/TF_GPU/lib/python3.10/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fc8c7bc6-9ec8-496c-a82e-621ecfef4fe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[273932.73253222]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca964e83-e787-4729-89ea-d4d425510802",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "316dacde-8438-409f-96b5-0a6f335f6af0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb5a35ed-fe02-40c3-8cb1-df112b94429d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61b68a0c-c8e2-4408-a1b2-05332b29a8a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eccbec16-455c-4dfa-9910-6921e0364196",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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